ISSN 1210-2512 (Print)

ISSN 1805-9600 (Online)



Proceedings of Czech and Slovak Technical Universities

About the Journal
Feature Articles
Editorial Board
Publishing Department
Society [CZ]

Log out
Your Profile

September 2022, Volume 31, Number 3 [DOI: 10.13164/re.2022-3]

Show all Hide all

S. M. Feng, S. Y. Wang, L. F. Wang [references] [full-text] [DOI: 10.13164/re.2022.0255] [Download Citations]
Two-Dimensional Signal Detection Algorithm for Omni-Directional Signal Receiving Using Low-Frequency Orthogonal Magnetic Antenna

This paper proposes a two-dimensional signal detection algorithm for low-frequency signal receiving using orthogonal magnetic antenna. According to the directional properties of a single antenna, the direction coefficient is introduced into the model. The algorithm based on Markov Chain Monte Carlo (MCMC) method can accurately estimate the direction coefficient and parameters of the noise in order to perform signal detection. The results show that the proposed algorithm is less affected by the direction of arrival and performs better, in terms of bit error rate, than that based on one-dimensional model. This study provides a valuable reference to omni-directional receiving of signals in low-frequency communication.

  1. ABRAHAM, D. A. Detection-threshold approximation for non-Gaussian backgrounds. IEEE Journal of Oceanic Engineering, 2010, vol. 35, no. 2, p. 355–365. DOI: 10.1109/JOE.2010.2043752
  2. SCHLEGEL, C., MALLAY, M., TOUESNARD, C. Atmospheric magnetic noise measurements in urban areas. IEEE Magnetics Letters, 2014, vol. 5, p. 1–4. DOI: 10.1109/LMAG.2014.2330337
  3. MCDONALD, K. F., BLUM, R. S. A statistical and physical mechanisms-based interference and noise model for array observations. IEEE Transactions on Signal Processing, 2000, vol. 48, no. 7, p. 2044–2056. DOI: 10.1109/78.847789
  4. GASDIA, F., MARSHALL, R. A. Assimilating VLF transmitter observations with an LETKF for spatial estimates of the D-region ionosphere. IEEE Transactions on Geoscience and Remote Sensing, 2020, vol. 58, no. 5, p. 3526–3543. DOI: 10.1109/TGRS.2019.2957716
  5. CHEN, Q. D., LIU, R., LIU, Y., et al. LF/VLF electromagnetic pulse measurement system. Chinese Journal of Radio Science, 2020, vol. 35, no. 5, p. 791–798. (In Chinese) DOI: 10.13443/j.cjors.2019092001
  6. GU, X., LI, G., PANG, H., et al. Statistical analysis of very low frequency atmospheric noise caused by the global lightning using ground-based observations in China. Journal of Geophysical Research: Space Physics, 2021, vol. 126, no. 6, p. 1–11. DOI: 10.1029/2020JA029101
  7. LOMBARDI, M. J. Bayesian inference for alpha-stable distribution: A random walk MCMC approach. Computation Statistics & Data Analysis, 2007, vol. 51, no. 5, p. 2688–2700. DOI: 10.1016/j.csda.2006.01.009
  8. YING, W. W., JIANG, Y. Z., LIU, Y. L. Parameters estimation for mixture model of atmospheric noise through MCMC method. Systems Engineering Electronics, 2012, vol. 34, no. 6, p. 1241–1245. (In Chinese) DOI: 10.3969/j.issn.1001-506X.2012.06.29
  9. YING, W. W., JIANG, Y. Z., LIU, Y. L. A blind receiver with multiple antennas in impulsive noise modeled as the sub-Gaussian distribution via the MCMC algorithm. IEEE Transactions on Vehicular Technology, 2013, vol. 62, no. 7, p. 3492–3497. DOI: 10.1109/TVT.2013.2250535
  10. WEISS, E., ALIMI, R. Low-Power and High-Sensitivity Magnetic Sensors and Systems. London (UK): Artech House, 2018. ISBN: 9781630812430
  11. RIPKA, P. (Ed.). Magnetic Sensors and Magnetometers. London (UK): Artech House, 2021. ISBN: 9781630817428
  12. FULLEKRUG, M., MEZENTSEV, A., WATSON, R., et al. Array analysis of electromagnetic radiation from radio transmitters for submarine communication. Geophysical Research Letters, 2014, vol. 41, no. 24, p. 9143–9149. DOI: 10.1002/2014GL062126
  13. GHAFFAR, A., AWAN, W. A., ZAIDI, A., et al. Compact ultra wide-band and tri-band antenna for portable device. Radioengineering, 2020, vol. 29, no. 4, p. 601–608. DOI: 10.13164/re.2020.0601
  14. ZAIDI, A., AWAN, W. A., HUSSAIN, N., et al. A wide and triband flexible antennas with independently controllable notch bands for sub-6-GHz communication system. Radioengineering, 2020, vol. 29, no. 1, p. 44–51. DOI: 10.13164/re.2020.0044
  15. AWAN, W. A., ALIBAKHSHIKENARI, M., LIMITI, E. A polydimethyl-siloxane based conformal ultra-wideband antenna with additional GSM band. In 2021 IEEE Asia-Pacific Microwave Conference (APMC). Brisbane (Australia), 2021, p. 67–69. DOI: 10.1109/APMC52720.2021.9661872
  16. KHALID, H., AWAN, W. A., HUSSAIN, M., et al. Design of an integrated sub-6 GHz and mmwave MIMO antenna for 5G handheld devices. Applied Sciences, 2021, vol. 11, no. 18, p. 1–21. DOI: 10.3390/app11188331
  17. ZAHRA, H., AWAN, W. A., ALI, W., et al. A 28 GHz broadband helical inspired end-fire antenna and its MIMO configuration for 5G pattern diversity applications. Electronics, 2021, vol. 10, no. 4, p. 1–15. DOI: 10.3390/electronics10040405
  18. HUSSAIN, N., AWAN, W. A., ALI, W., et al. Compact wideband patch antenna and its MIMO configuration for 28 GHz applications. AEU-International Journal of Electronics and Communications, 2021, vol. 132, p. 1–8. DOI: 10.1016/j.aeue.2021.153612
  19. SHI, W., LI, G. M., QIN, J. P. A method for directional receiving of VLF signals with magnetic antennas. In ICWMMN 2013-5th IET International Conference on Wireless, Mobile & Multimedia Networks. Beijing (China), 2013, p. 51–54. DOI: 10.1049/cp.2013.2376
  20. KHAN, M. A., SUN, J., LI, B. D., et al. Magnetic sensors–A review and recent technologies. Engineering Research Express, 2021, vol. 3, no. 2, p. 1–22. DOI: 10.1088/2631-8695/ac0838
  21. WEI, S., LIAO, X., ZHANG, H., et al. Recent progress of fluxgate magnetic sensors: Basic research and application. Sensors, 2021, vol. 21, no. 4, p. 1–18. DOI: 10.3390/s21041500
  22. GUO, H. B. The research and realization of active omni directional small loops antenna loaded ferrite core. Harbin: Harbin Engineering University, 2007. (In Chinese) DOI: 10.7666/d.y1097838
  23. FU, T. H., SU, M. Research on long wave communication technique for UUV. Journal of Sichuan Ordnance, 2013, vol. 34, no. 3, p. 83–85. (In Chinese) DOI: 10.11809/scbgxb2013.03.024
  24. XIE, C. F., RAO, K. J., ZHAO, J. S. Electromagnetic Fields and Waves. 5nd ed. Beijing (China): Higher Education Press, 2019. (In Chinese) ISBN: 9787040525182
  25. SONG, Z., ZHANG, J. H., HUANG, Z. Antenna and Propagation. 3nd ed. Xi’an (China): Xidian University Publishing House, 2016. (In Chinese) ISBN: 9787560640563
  26. ROBERT, C. P., ELVIRA, V., TAWN, N., et al. Accelerating MCMC algorithms. Wiley Interdisciplinary Reviews: Computational Statistics, 2018, vol. 10, no. 5, p. 1–14. DOI: 10.1002/wics.1435

Keywords: Orthogonal antenna, omni-directional receiving, parameters estimation, non-Gaussian noise

H. S. Zhu, W. H. Hu, B. F. Guo, X. X. Zhu, D. F. Xue, C. A. Zhu [references] [full-text] [DOI: 10.13164/re.2022.0262] [Download Citations]
Bistatic ISAR sparse aperture maneuvering target translational compensation imaging algorithm

For bistatic inverse synthetic aperture radar (Bi-ISAR), the non-uniform motion state of maneuvering target and the time-varying bistatic angle make the traditional imaging method of moving target face the problem of translation compensation, and the traditional translation compensation method is not suitable for the return wave in the case of sparse aperture. In this paper, a compensation imaging method combining two-dimension joint linearized Bregman iteration and image contrast search is proposed. The translation compensation problem can be transformed into two-dimension joint compressed sensing sparse reconstruction and moving target motion parameter estimation. The proposed algorithm makes use of the gain of echo two-dimension compression, greatly improves the accuracy of translation compensation and the quality of target image and has strong robustness to noise. The processing results of simulation data verify the effectiveness and superiority of the algorithm.

  1. ZHU, X. X., SHI, L., HU, W. H., et al. Bi-ISAR sparse imaging algorithm with complex Gaussian scale mixture prior. IET Radar, Sonar & Navigation, 2019, vol. 13, no. 12, p. 2202–2211. DOI: 10.1049/iet-rsn.2019.0296
  2. CABRERA, S. D., PARKS, T. W. Extrapolation and spectral estimation with iterative weighted norm modification. IEEE Transactions on Signal Processing, 1991, vol. 39, no. 4, p. 842–851. DOI: 10.1109/78.80906
  3. KIM, K. T., KIM, S. W., KIM, H. T. Two-dimension ISAR imaging using full polarisation and super-resolution processing techniques. IET Radar, Sonar and Navigation, 1998, vol. 145, no. 4, p. 240 to 246. DOI: 10.1049/ip-rsn:19982033
  4. LARSSON, E. G., LI, J., Spectral analysis of periodically gapped data. IEEE Transactions on Aerospace and Electronic Systems, 2003, vol. 39, no. 3, p. 1089–1097. DOI: 10.1109/TAES.2003.1238761
  5. WANG, Y., LIU, Q. Super-resolution sparse aperture ISAR imaging of maneuvering target via the RELAX algorithm. IEEE Sensors Journal, 2018, vol. 18, no. 21, p. 8726–8738. DOI: 10.1109/JSEN.2018.2868308
  6. XU, X. J., NARAYANAN, R. M., et al. Enhanced resolution in SAR/ISAR imaging using iterative sidelobe apodization. IEEE Transactions on Image Processing, 2005, vol. 14, no. 4, p. 537 to 547. DOI: 10.1109/tip.2004.841198
  7. LI, S. D., CHEN, Y. B., YANG, J., et al. Imagine method with high resolution for maneuvering target based on compressive sensing. Journal of Air Force Early Warning Academy, 2015, vol. 29, no. 5, p. 313–321. (In Chinese)
  8. ZHANG, S., SUN, S., ZHANG, W., et al. High-resolution bistatic ISAR image formation for high-speed and complex-motion targets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, vol. 8, no. 7, p. 3520–3531. DOI: 10.1109/JSTARS.2015.2417192
  9. ZHANG, S., LIU, Y., LI, X., et al. Autofocusing for sparse aperture ISAR imaging based on joint constraint of sparsity and minimum entropy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, vol. 10, no. 3, p. 998–1011. DOI: 10.1109/JSTARS.2016.2598880
  10. KANG, M., LEE, S., KIM, K., et al. Bistatic ISAR imaging and scaling of highly maneuvering target with complex motion via compressive sensing. IEEE Transactions on Aerospace and Electronic Systems, 2018, vol. 54, no. 6, p. 2809–2826. DOI: 10.1109/TAES.2018.2830598
  11. LI, S. Y., ZHAO, G. Q., ZHANG, W., et al. ISAR imaging by twodimensional convex optimization-based compressive sensing. IEEE Sensors Journal, 2016, vol. 16, no. 19, p. 7088–7093. DOI: 10.1109/JSEN.2016.2599540
  12. WANG, W., HU, Z., HUANG, P. 3-D MIMO radar imaging of ship target with rotational motions. Radioengineering, 2019, vol. 28, no. 4, p. 776–784. DOI: 10.13164/re.2019.0776
  13. DONOHO, D. L. Compressed sensing. IEEE Transactions on Information Theory, 2006, vol. 52, no. 4, p. 1289–1306. DOI: 10.1109/TIT.2006.871582
  14. YU, T., DENG, S. J. Research of remote sensing image compression technology based on compressed sensing. In Tan, T., Ruan, Q., Wang, S., et al. (Eds.) Advances in Image and Graphics Technologies (Proceedings of the 10th Chinese Conference IGTA), 2015, p. 214–223. DOI: 10.1007/978-3-662-47791-5_25
  15. YANG, J. G., JIN, T., XIAO, C., et al. Compressed sensing radar imaging: Fundamentals, challenges, and advances. Sensors, 2019, vol. 19, no. 14, p. 1–19. DOI: 10.3390/s19143100
  16. DUAN, H. P., ZHANG, L. Z., FANG, J., et al. Pattern-coupled sparse Bayesian learning for inverse synthetic aperture radar imaging. IEEE Signal Processing Letters, 2015, vol. 22, no. 11, p. 1995–1999. DOI: 10.1109/LSP.2015.2452412
  17. ZHU, X. X., GUO, B. F., HU, W. H., et al. Scene segmentation of multi-band ISAR fusion imaging based on MB-PCSBL. IEEE Sensors Journal, 2021, vol. 21, no. 3, p. 3520–3532. DOI: 10.1109/JSEN.2020.3026109
  18. ZHANG, D., ZHANG, Y. S., FENG, C. Q., et al. Joint-2D-SL0 algorithm for joint sparse matrix reconstruction. International Journal of Antennas and Propagation, 2017, p. 1–7. DOI: 10.1155/2017/6862852
  19. ZHANG, L., SHENG, J. L., DUAN, J., et al. Translational motion compensation for ISAR imaging under low SNR by minimum entropy. EURASIP Journal on Advances in Signal Processing, 2013, p. 1–19. DOI: 10.1186/1687-6180-2013-33
  20. ZHANG, S. S., ZHANG, W., ZONG, Z. L., High-resolution bistatic ISAR imaging based on two-dimensional compressed sensing. IEEE Transactions on Antennas and Propagation, 2015, vol. 63, no. 5, p. 2098–2011. DOI: 10.1109/TAP.2015.2408337
  21. ZHU, X. X., LIU, L. M., GUO, B. F., et al. Two-dimensional multiradar ISAR fusion imaging based on fast linearized Bregman iteration algorithm. Journal of Applied Remote Sensing, 2021, vol. 15, no. 2, p. 1–21. DOI: 10.1117/1.JRS.15.026507
  22. FENG, J. J., ZHANG, G. ISAR imaging based on iterative reweighted Lp block sparse reconstruction algorithm. Progress In Electromagnetics Research M, 2016, vol. 48, p. 155–162. DOI: 10.2528/pierm16041501

Keywords: Bistatic-ISAR, maneuvering target, sparse aperture, translational compensation

B. Minnaert [references] [full-text] [DOI: 10.13164/re.2022.0273] [Download Citations]
Unified Expression of the Conjugate Image Impedances for Two-port Representations

Conjugate image impedances are used to minimize power reflections in a variety of domains, including amplifier design, microwave engineering, wireless power transfer, antenna design and millimeter wave applications. For a two-port network, they can be described as function of different parameters including impedance, admittance, hybrid, inverse hybrid, chain, scattering and chain scattering parameters. In this work, a general unified structure for the conjugate image impedances is provided, valid for each of the two-port representations. It highlights its close relationship with the Rollett stability factor and provides insight into the structure of conjugate image impedances.

  1. ROBERTS, S. Conjugate-image impedances. Proceedings of the IRE, 1946, vol. 34, no. 4, p. 198–204. DOI: 10.1109/JRPROC.1946.234242
  2. POZAR, D. M. Microwave Engineering. 4th ed., JohnWiley & Sons, 2011. ISBN: 0470631554
  3. INAGAKI, N. Theory of image impedance matching for inductively coupled power transfer systems. IEEE Transactions on Microwave Theory and Techniques, 2014, vol. 62, no. 4, p. 901–908. DOI: 10.1109/TMTT.2014.2300033
  4. MINNAERT, B., STEVENS, N. Conjugate image theory applied on capacitive wireless power transfer. Energies, 2017, vol. 10, no. 1, p. 1–15. DOI: 10.3390/en10010046
  5. HONG, J.-S., LANCASTER, M. J. Microstrip Filters for RF/Microwave Applications. John Wiley & Sons, 2004. ISBN: 9780471388777
  6. DOBROWOLSKI, J. Microwave Network Design using the Scattering Matrix. Artech House, 2010. ISBN: 9781608071296
  7. YARMAN, B. S. Design of Ultra Wideband Antenna Matching Networks: Via Simplified Real Frequency Technique. Springer Science & Business Media, 2008. ISBN: 9781402084171
  8. MINNAERT, B., STEVENS, N. Single variable expressions for the efficiency of a reciprocal power transfer system. International Journal of Circuit Theory and Applications 2017, vol. 45, no. 10, p. 1418–1430. DOI: 10.1002/cta.2299
  9. HUANG, K. C., WANG, Z. Millimeter Wave Communication Systems. John Wiley & Sons, 2011. ISBN: 9780470404621
  10. FRICKEY, D. A. Conversions between S, Z, Y, H, ABCD, and T parameters which are valid for complex source and load impedances. IEEE Transactions on Microwave Theory and Techniques 1994, vol. 42, no. 2, p. 205–211. DOI: 10.1109/22.275248
  11. COOMAN, A. Conversions between electrical network representations. arXiv:1607.03642, 2016, p. 1–3. DOI: 10.48550/arXiv.1607.03642
  12. DIONIGI, M., KOZIEL, S., MONGIARDO, M., et al. Iterative determination of conjugate image impedances for N-port networks. In IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO). Ottawa, (Canada), 2015, p. 1–4. DOI: 10.1109/NEMO.2015.7415007
  13. OHIRA, T. Extended kQ product formulas for capacitiveand inductive-coupling wireless power transfer schemes. IEICE Electronics Express, 2014, vol. 11, no. 9, p. 1–7. DOI: 10.1587/elex.11.20140147
  14. ROLLETT, J. Stability and power-gain invariants of linear twoports. IRE Transactions on Circuit Theory, 1962, vol. 9, no. 1, p. 29–32. DOI: 10.1109/TCT.1962.1086854
  15. ROLLETT, J. Some Invariant Properties of Linear Electrical Circuits. PhD thesis. University of Surrey, 1971.
  16. CARLI, E., CORZANI, T. General representation for the Rollett stability factor of a two-port network. IEEE Transactions on Circuit Theory, 1969, vol. 16, no. 2, p. 215–217. DOI: 10.1109/TCT.1969.1082928
  17. REVEYRAND, T. Multiport conversions between S, Z, Y, h, ABCD, and T parameters. In IEEE International Workshop on Integrated Nonlinear Microwave and Millimetre-wave Circuits (INMMIC). Brive La Gaillarde (France), 2018, p. 1–3. DOI: 10.1109/INMMIC.2018.8430023
  18. WOODS, D. Multiport-network analysis by matrix renormalisation employing voltage-wave S-parameters with complex normalisation. Proceedings of the Institution of Electrical Engineers, 1977, vol. 124, no. 3, p. 198–204. DOI: 10.1049/piee.1977.0037
  19. MASTRI, F, MONGIARDO, M., MONTI, G., et al. Characterization of wireless power transfer links by network invariants. In IEEE International Conference on Electromagnetics in Advanced Applications (ICEAA). Verona (Italy), 2017, p. 590–593. DOI: 10.1109/ICEAA.2017.8065314
  20. MARKS, R. B., WILLIAMS, D. F. A general waveguide circuit theory. Journal of Research of the National Institute of Standards and Technology, 1992, vol. 97, no. 5, p. 533. DOI: 10.6028/jres.097.024
  21. AMAKAWA, S. Scattered reflections on scattering parameters - Demystifying complex-referenced S parameters. IEICE Transactions on Electronics, 2016, vol. 99, no. 10, p. 1100–1112. DOI: 10.1587/transele.E99.C.1100
  22. WILLIAMS, D. Traveling waves and power waves: Building a solid foundation for microwave circuit theory. IEEE Microwave Magazine, 2013, vol. 14, no. 7, p. 38–45. DOI: 10.1109/MMM.2013.2279494
  23. LLORENTE-ROMANO, S., GARCA-LAMPEREZ, A., SARKAR, T. K., et al. An exposition on the choice of the proper S parameters in characterizing devices including transmission lines with complex reference impedances and a general methodology for computing them. IEEE Antennas and Propagation Magazine, 2013, vol. 55, no. 4, p. 94–112. DOI: 10.1109/MAP.2013.6645145

Keywords: Circuit theory, conjugate image, two-port networks

E. M. Cheng, K. Y. Lee, S. F. Khor, N. F. Mohd Nasir, C. W. S. R. Mohamad, N. A. Abdul Aziz, E. Z. Mohd Tarmizi, S. A. Baharuddin [references] [full-text] [DOI: 10.13164/re.2022.0281] [Download Citations]
Microwave Dielectric and Reflection Analysis on Pure and Adulterated Trigona Honey and Honey Gold

Honey adulteration is common in food industry, as it provides cheaper alternative for user to consume honey. However, it has been abused by industry runners with unsavory practices. It leads to business fraudulency. Pure honey is very precious due to its powerful health-giving properties. It rises attention of beekeeper, wholesaler, food manufacturer, retailer and consumer because this issue has been sensationally reported in media mass. Enforcement of law is initiated to mitigate the abuse and fraudulency. Apart from that, it motivates scientists, technologists and engineers to strive for an effective solution. Microwave sensing method is well known in agricultural product and food. Hence, dielectric and reflection response is explored for the potential of development of instrumentation system in gauging edible honeys. In this work, the dielectric and reflection measurement were conducted using Agilent E8362B PNA Network Analyzer in conjunction with Agilent 85070E Performance Probe from 0.5 GHz to 4.5 GHz. Dielectric and reflection measurement were conducted to investigate its dielectric behavior and mismatch impedance due to water and sucrose content in honey. It can be noticed that dielectric constant, ε’ decreases when frequency increases. In the meantime, ε’ decreases with increment of water and sucrose content for Honey Gold and Trigona Honey. Meanwhile, for water adulterated Honey Gold and Trigona Honey, loss factor, ε” decrease when frequencies increases. In addition, ε” decreases when water content < 36% and < 43% for Honey Gold and Trigona Honey, respectively. It can be found that at 1 GHz to 4 GHz, ε” increases when sucrose content increases which applicable for Honey Gold and Trigona Honey. In reflection measurement, magnitude of reflection coefficient, |Γ| decrease when frequency increases for all percentage of water and sucrose content for both honeys. Withal, phase, -φ increases as frequency increases for both water adulterated honeys. -φ varies insignificantly when sucrose content increases for both sucrose adulterated honeys.

  1. ZABRODSKA, B., VORLOVA, L. Honey quality inspection and adulteration identification. Acta Veterinaria Brno, 2015, vol. 83, no. 10, p. S85–S102. DOI: 10.2754/avb201483S10S85
  2. VANDAMME, L., HEYNEMAN, A., HOEKSEMA, H., et al. Honey in modern wound care : A systematic review. Burns, 2013, vol. 39, no.8, p. 1514–1525. DOI: 10.1016/j.burns.2013.06.014
  3. MARGHITAS, L. A., DEZMIREAN, D., MOISE, A., et al. Physicochemical and bioactive properties of different floral origin honeys from Romania. Food Chemistry, 2009, vol. 112, no. 4, p. 863–867. DOI: 10.1016/j.foodchem.2008.06.055
  4. BALL, J. A. R., HORSFIELD, B., HOLDEM, J. R., et al. Cheese curd moisture measurement using a 6-port reflectometer. In Asia Pacific Microwave Conference. New Delhi (India), 1996, vol. 2, p. 479–482.
  5. CHENG, E. M., SHAHRIMAN, A. B., RAHIM, H., et al. Microwave reflection based dielectric spectroscopy for moisture content in Melele mango fruit (Mangifera Indica L.). Journal of Telecommunication, Electronic and Computer Engineering, 2018, vol. 10, no. 1–14, p. 1–6. e-ISSN: 2289-8131
  6. GUO, W., TRABELSI, S., NELSON, S. O., et al. Storage effects on dielectric properties of eggs from 10 to 1800 MHz. Journal of Food Science, 2007, vol. 72, no. 5, p. 335–340. DOI: 10.1111/j.1750-3841.2007.00392.x
  7. AHN, J. Y., KIL, D. Y., KONG, C., et al. Comparison of ovendrying methods for determination of moisture content in feed ingredients. Asian-Australasian Journal of Animal Sciences, 2014, vol. 27, no. 11, p. 1615–1622. DOI: 10.5713/ajas.2014.14305
  8. GUO, W., ZHU, X., LIU, Y., et al. Sugar and water contents of honey with dielectric property sensing. Journal of Food Engineering, 2010, vol. 97, no. 2, p. 275–281. DOI: 10.1016/j.jfoodeng.2009.10.024
  9. GUO, W., LIU, Y., ZHU, X., et al. Dielectric properties of honey adulterated with sucrose syrup. Journal of Food Engineering, 2011, vol. 107, no. 1, p. 1–7. DOI: 10.1016/j.jfoodeng.2011.06.013
  10. CALAY, R. K., NEWBOROUGH, M., PROBERT, D., et al. Predictive equations for the dielectric properties of foods. International Journal of Food Science and Technology, 1995, vol. 29, no. 6, p. 699–713. DOI: 10.1111/j.1365-2621.1994.tb02111.x
  11. KOMAROV, V., WANG, S., TANG, J. Permittivity and measurement. In Encyclopedia of RF and Microwave Engineering. 1st ed. New York (United States): John Wiley and Sons, 2005. Online ISBN: 9780471654506 DOI: 10.1002/0471654507.eme308
  12. PURANIK, S., KUMBHARKHANE, A., MEHROTRA, S. Dielectric properties of honey-water mixtures between 10 MHz and 10 GHz using time domain technique. Journal of Microwave Power and Electromagnetic Energy, 1991, vol. 26, no. 4, p. 196–201. DOI: 10.1080/08327823.1991.11688157
  13. AHMED, J., PRABHU, S. T., RAGHAVAN, G. S. V., et al. Physico-chemical, rheological, calorimetric and dielectric behaviour of selected Indian honey. Journal of Food Engineering, 2007, vol. 79, no. 4, p. 1207–1213. DOI: 10.1016/j.jfoodeng.2006.04.048
  14. PENG, S., LING, N., ANIZA, Y., et al. Total phenolic contents and colour intensity of Malaysian honeys from the Apis spp. and Trigona spp. bees. Agriculture and Agricultural Science Procedia, 2014, vol. 2, p. 150–155. DOI: 10.1016/j.aaspro.2014.11.022
  15. SHUKLA, A., TEWARI, P. R., KHAN, G. Study of phenol and its derivatives based on dipole moment. Journal of Chemical and Pharmaceutical Research, 2011, vol. 3, no. 1, p. 79–83. ISSN: 0975-7384
  16. KEK, S. P., CHIN, N. L., TAN, S. W., et al. Classification of honey from its bee origin via chemical profiles and mineral content. Food Analytical Methods, 2017, vol. 10, no. 1, p. 19–30. DOI: 10.1007/s12161-016-0544-0
  17. UENO, S. (Ed.) Biological Effects of Magnetic and Electromagnetic Fields. 1st ed. New York (United States): Springer, 1996. ISBN: 978-0-585-31661-1
  18. GALGANI, J., AGUIRRE, C., DIAZ, E. Acute effect of meal glycemic index and glycemic load on blood glucose and insulin responses in humans. Nutrition Journal, 2006, vol. 5, no. 22, p. 1–7. DOI: 10.1186/1475-2891-5-22
  19. KUMAR, M., GUPTA, R. Diamagnetic Susceptibility of Organic Compounds, Oils, Paraffins and Polyethylenes. 1st ed. Berlin (Germany): Springer Berlin, Heidelberg, 2008. ISBN: 978-3-540-45860-9

Keywords: Honey, sucrose, water, dielectric, reflection

H. Jiang, X. Y. Cao, H. H. Yang, J. Gao, L. Ji-Di [references] [full-text] [DOI: 10.13164/re.2022.0295] [Download Citations]
Single-Layer Broadband Endfire Antenna with High-Gain and Stable Beams Based on Spoof Surface Plasmon Polaritons

A single-layer broadband endfire antenna with high-gain and stable beams based on the spoof surface plasmon polaritons (SSPPs) is proposed in this letter. The amplitude and phase of the surface wave are controlled by asymmetric protrusions on both sides. The anti-symmetric structure is added to balance the upper and lower electric fields while adjusting the impedance matching at the same time. Eventually, endfire radiation is generated with a stable beam to the free space within 5.25-7.94 GHz to form a relative bandwidth of 40.8%. The maximum achieved gain is 11.7 dBi at 7.1 GHz. The experimental results are basically consistent with the simulations. The antenna with high-gain, broadband and a stable beam can be effectively used in wireless communication systems.

  1. BARNES, W. L., DEREUX, A., EBBESEN, T. W. Surface plasmon subwavelength optics. Nature, 2003, vol. 424, no. 6950, p. 824–830. DOI: 10.1038/nature01937
  2. TARASOV, Y. V., IAKUSHEV, D. A., USATENKO, O. V. Plasmon-polariton excitations on surfaces with fluctuating impedance. In The 2016 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW). 2016, p. 1–4. DOI: 10.1109/MSMW.2016.7538190
  3. JAISWAL, R. K., PANDIT, N., PATHAK, N. P. Spoof surface plasmon polaritons based reconfigurable band-pass filter. IEEE Photonics Technology Letters, 2018, vol. 31, no. 3, p. 218–221. DOI: 10.1109/LPT.2018.2889007
  4. WEI, D., LI, J., YANG, J., et al. Wide-scanning-angle leaky-wave array antenna based on microstrip SSPPs-TL. IEEE Antennas and Wireless Propagation Letters, 2018, vol. 17, no. 8, p. 1566–1570. DOI: 10.1109/LAWP.2018.2855178
  5. YIN, J., BAO, D., REN, J., et al. Endfire radiations of spoof surface plasmon polaritons. IEEE Antennas and Wireless Propagation Letters, 2017, vol. 16, p. 597–600. DOI: 10.1109/LAWP.2016.2592512
  6. LI, S., ZHANG, Q., XU, Z., et al. Phase transforming based on asymmetric spoof surface plasmon polariton for endfire antenna with sum and difference beams. IEEE Transactions on Antennas and Propagation, 2020, vol. 68, no. 9, p. 6602–6613. DOI: 10.1109/TAP.2020.2993083
  7. LIU, L., CHEN, M., YIN, X. Single-layer high gain endfire antenna based on spoof surface plasmon polaritons. IEEE Access, 2020, vol. 8, p. 64139–64144. DOI: 10.1109/ACCESS.2020.2984153
  8. LIU, P., FENG, H., LI, Y., et al. Low-profile endfire leaky-wave antenna with air media. IEEE Transactions on Antennas Propagation, 2018, vol. 66, no. 3, p. 1086–1092. DOI: 10.1109/TAP.2018.2790042
  9. ZHAO, Y., SHEN, Z., WU, W. Wideband and low-profile H-plane ridged SIW horn antenna mounted on a large conducting plane. IEEE Transaction on Antennas Propagation, 2014, vol. 62, no. 11, p. 5895–5900. DOI: 10.1109/TAP.2014.2354420
  10. ZHAI, G., CHENG, Y., YIN, Q., et al. Gain enhancement of printed log-periodic dipole array antenna using director cell. IEEE Transactions on Antennas and Propagation, 2014, vol. 62, no. 11, p. 5915–5919. DOI: 10.1109/TAP.2014.2355851
  11. SHI, J., ZHU, L., LIU, N. W., et al. A microstrip Yagi antenna with an enlarged beam tilt angle via a slot-loaded patch reflector and pin-loaded patch directors. IEEE Antennas Wireless and Propagation Letters, 2019, vol. 18, no. 4, p. 679–683. DOI: 10.1109/LAWP.2019.2901033
  12. TIAN, D., XU, R., PENG, G., et al. Low profile high-efficiency bidirectional endfire antenna based on spoof surface plasmon polaritons. IEEE Antennas and Wireless Propagation Letters, 2018, vol. 17, no. 5, p. 837–840. DOI: 10.1109/LAWP.2018.2818109
  13. ZHANG, X.-F., SUN, W. J., CHEN, J. X. Millimeter-wave ATS antenna with wideband-enhanced endfire gain based on coplanar plasmonic structures. IEEE Antennas and Wireless Propagation Letters, 2019, vol. 18, no. 5, p. 826–830. DOI: 10.1109/LAWP.2019.2902874
  14. KANDWAL, A., ZHANG, Q., TANG, X. L., et al. Low-profile spoof surface plasmon polaritons traveling-wave antenna for nearendfire radiation. IEEE Antennas and Wireless Propagation Letters, 2018, vol. 17, no. 2, p. 184–187. DOI: 10.1109/LAWP.2017.2779455
  15. ZHUANG, K., GENG, J., WANG, K., et al. Pattern reconfigurable antenna applying spoof surface plasmon polaritons for wide angle beam steering. IEEE Access, 2019, vol. 7, p. 15444–15451. DOI: 10.1109/ACCESS.2019.2895106
  16. GE, S., ZHANG, Q., RASHID, A. K., et al. Analysis of asymmetrically corrugated Goubau-line antenna for endfire radiation. IEEE Transactions on Antennas and Propagation, 2019, vol. 67, no. 11, p. 7133–7138. DOI: 10.1109/TAP.2019.2927633

Keywords: Spoof surface plasmon polaritons (SSPPs), endfire antennas, stable beams, broadband antennas

W. Jlassi, R. Haddad, R. Bouallegue, R. Shubair [references] [full-text] [DOI: 10.13164/re.2022.0301] [Download Citations]
Increase of the Lifetime of Wireless Sensor Network using Clustering Algorithm and Optimal Path Selection Method

By the recent improvement of the internet of things (IoT), the need to implement wireless networks is increasing. It is a challenge to balance between battery lifetime of the different sensors and network lifetime. Many studies proved the importance of using clustering and mobile data collectors (MDCs) to extend the operating time of sensor nodes. A mobile data collector is used to gather the data recorded by the nodes over a short transmission range. The proposed approach aims to decrease the energy consumption of each sensor node by using the Genetic Algorithm (GA) and mobile data collector. So, we suggest a clustering algorithm to find suitable Cluster Heads and form clusters. Then, we employ the Genetic algorithm to construct an optimal data gathering path for MDC. Computer simulation proves that the proposed approach outperforms existing ones.

  1. ROCHA, A., PIRMEZ, L., DELICATO, F., et al. WSNs clustering based on semantic neighbourhood relationships. Computer Networks Journal, 2012, vol. 56, no. 5, p. 1627–1645. DOI: 10.1016/j.comnet.2012.01.014
  2. KULAKOWSKI, P., CALLE, E., MARZO, J. L. Performance study of wireless sensor and actuator networks in forest fire scenarios. International Journal of Communication Systems, 2013, vol. 26, no. 4, p. 515–529. DOI: 10.1002/dac.2311
  3. YICK, J., MUKHERJEE, B., GHOSAL, D. Wireless sensor network survey. Computer Networks, 2008, vol. 52, no. 12, p. 2292– 2330. DOI: 10.1016/j.comnet.2008.04.002
  4. MANJUNATHA, T. N., SUSHMA, M. D., SHIVAKUMAR, K. M. Security concepts and Sybil attack detection in wireless sensor networks. International Journal of Emerging Trends and Technology in Computer Science, 2013, vol. 2, no. 2, p. 383–390. ISSN: 2278-6856
  5. CHENG, S., CHANG, T. Y. An adaptive learning scheme for load balancing with zone partition in multi-sink wireless sensor network. Expert Systems with Applications, 2012, vol. 39, no. 10, p. 9427–9434. DOI: 10.1016/j.eswa.2012.02.119
  6. EL FISSAOUI, M., BENI-HSSANE, A., SAADI, M. Energy efficient and fault tolerant distributed algorithm for data aggregation in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 2019, vol. 10, no. 2, p. 569–578. DOI: 10.1007/s12652-018-0704-8
  7. ABDOLKARIMI, M., ADABI, S., SHARIFI, A. A new multiobjective distributed fuzzy clustering algorithm for wireless sensor networks with mobile gateways. AEU-International Journal of Electronics and Communications, 2018, vol. 89, p. 92–104. DOI: 10.1016/j.aeue.2018.03.020
  8. SHAH, R. C., ROY, S., JAIN, S., et al. Data MULEs: Modeling a three-tier architecture for sparse sensor networks. In Proceedings of IEEE Workshop on Sensor Network Protocols and Applications (SNPA). Anchorage (AK, USA), 2003, p. 30–41. DOI: 10.1109/SNPA.2003.1203354
  9. CHEN, T., CHEN, T., WU, P. On data collection using mobile robot in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2011, vol. 41, no. 6, p. 1213–1224. DOI: 10.1109/TSMCA.2011.2157132
  10. ZHAO, M., MA, M., YANG, Y. (2008). Mobile data gathering with space-division multiple access in wireless sensor network. In Proceedings of 27th Conference on Computer Communications (INFOCOM). Phoenix (AZ, USA), 2008, p. 1957–1965. DOI: 10.1109/INFOCOM.2008.185
  11. SALARIAN, H., CHIN, K.-W., NAGHDY, F. An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Transactions on Vehicular Technology, 2014, vol. 63, no. 5, p. 2407–2419. DOI: 10.1109/TVT.2013.2291811
  12. DONG, M., OTA, K., YANG, L. T., et al. LSCD: A low-storage clone detection protocol for cyber-physical systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2016, vol. 35, no. 5, p. 712–723. DOI: 10.1109/TCAD.2016.2539327
  13. DUAN, X., ZHAO, C., HE, S., et al., Distributed algorithms to compute Walrasian equilibrium in mobile crowdsensing. IEEE Transactions on Industrial Electronics, 2017, vol. 64, no. 5, p. 4048–4057. DOI: 10.1109/TIE.2016.2645138
  14. GHOSH, N., BANERJEE, I., SHERRATT, R. S. On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network. Wireless Networks, 2019, vol. 25, no. 4, p. 1829–1845. DOI: 10.1007/s11276-017-1635-6
  15. SERT, S. A., BAGCI, H., YAZICI, A. MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 2015, vol. 30, p. 151–165. DOI: 10.1016/j.asoc.2014.11.063
  16. SOMASUNDARA, A. A, RAMAMOORTHY, A., SRIVASTAVA, M. B. Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines. In 25th IEEE International Real-Time Systems Symposium. Lisbon (Portugal), 2004, p. 296–305. DOI: 10.1109/REAL.2004.31
  17. ZHANG, C., FEI, S. A matching game-based data collection algorithm with mobile collectors. Sensors, 2020, vol. 20, no. 5, p. 1–16. DOI: 10.3390/s20051398
  18. YALÇIN, S., ERDEM, E. A mobile sink path planning for wireless sensor networks based on priority-ordered dependent nonparametric trees. International Journal of Communication Systems, 2020, vol. 33, no. 12, p. 1–19. DOI: 10.1002/dac.4449
  19. YOUNES, A., BADAWI, U. A., FARAG, T. H., et al. A genetic algorithm to find the minimum cost paths tree with bandwidth constraint in the computer networks. International Journal of Applied Engineering Research, 2018, vol. 13, no. 10, p. 7472–7476. ISSN: 0973-4562
  20. KAMAREI, M., PATOOGHY, A., SHAHSAVARI, Z., et al. Lifetime expansion in WSNs using mobile data collector: A learning automata approach. Journal of King Saud University - Computer and Information Sciences, 2018, vol. 32, no. 1, p. 65–72. DOI: 10.1016/j.jksuci.2018.03.006
  21. ALPARSLAN, D. N., SOHRABY, K. Two-dimensional modeling and analysis of generalized random mobility models for wireless ad hoc networks. IEEE/ACM Transactions on Networking, 2007, vol. 15, no. 3, p. 616–629. DOI: 10.1109/TNET.2007.893873
  22. WU, Q., SUN, P., BOUKERCHE, A. A novel data collector path optimization method for lifetime prolonging in wireless sensor networks. In 2019 IEEE Global Communications Conference (GLOBECOM). Waikoloa (HI, USA), 2019, p. 1–6. DOI: 10.1109/GLOBECOM38437.2019.9013989
  23. WEN, W., ZHAO, S., SHANG, C. EAPC: Energy-aware path construction for data collection using mobile sink in wireless sensor networks. IEEE Sensors Journal, 2020, vol. 18, no. 2, p. 890–901. DOI: 10.1109/JSEN.2017.2773119
  24. HA, I., DJURAEV, M., AHN, B. An optimal data gathering method for mobile sinks in WSNs. Wireless Personal Communication, 2017, vol. 97, p. 1401–1417. DOI: 10.1007/s11277-017-4579-3
  25. RAO, X., HUANG, H., TANG, J., et al. Residual energy-aware mobile data gathering in wireless sensor networks. Journal of Telecommunications Systems, 2016, vol. 62, p. 31–41. DOI: 10.1007/s11235-015-9980-1
  26. SUN, Y., DONG, W., CHEN, Y. An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Communications Letters, 2019, vol. 21, no. 6, p. 1317–1320. DOI: 10.1109/LCOMM.2017.2672959
  27. ABIDOYE, A. P., KABASO, B. Energy-efficient hierarchical routing in wireless sensor networks based on fog computing. EURASIP Journal on Wireless Communications and Networking, 2021, p. 1–26. DOI: 10.1186/s13638-020-01835-w
  28. JLASSI, W., HADDAD, R., BOUALLEGUE, R., et al. A combination of K-means algorithm and optimal path selection method for lifetime extension in wireless sensor networks. In International Conference on Advanced Information Networking and Applications. 2021, p. 416–425. DOI: 10.1007/978-3-030-75078-7_42
  29. EL FISSAOUI, M., BENI-HSSANE, A., SAADI, M. Energy aware hybrid scheme of client-server and mobile agent models for data aggregation in wireless sensor networks. In Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). 2016, p. 227–232. DOI: 10.1007/978-3-319-52941-7_23
  30. TASHTARIAN, F., MOGHADDAM, M. H. Y., SOHRABY, K., et al. On maximizing the lifetime of wireless sensor networks in event-driven applications with mobile sinks. IEEE Transactions on Vehicular Technology, 2015, vol. 64, no. 7, p. 3177–3189. DOI: 10.1109/TVT.2014.2354338
  31. MAHESHWARI, P., SHARMA, A., VERMA, K. Energy efficient cluster-based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 2021, vol. 110, p. 1–15. DOI: 10.1016/j.adhoc.2020.102317
  32. LAXMA REDDY, D., PUTTAMADAPPA, C., SURESHB, H. N. Merged glowworm swarm with ant colony optimization for energy efficient clustering and routing in wireless sensor network. Pervasive and Mobile Computing, 2021, vol. 71, p. 1–18. DOI: 10.1016/j.pmcj.2021.101338

Keywords: Wireless sensor network, genetic algorithm, hierarchical agglomerative clustering algorithm, clustering, mobile data collector

S. Sreenu, N. Kalpana [references] [full-text] [DOI: 10.13164/re.2022.0312] [Download Citations]
Innovative Power Allocation Strategy for NOMA Systems by Employing the Modified ABC Algorithm

Non-Orthogonal Multiple Access (NOMA) technique is a remarkable component of 5G wireless networks; since NOMA immensely augments the spectral efficiency and serves all users fairly. To accomplish these, efficient power allocation is crucial for improving the NOMA system's performance. Accordingly, in this article, we formulate a power allocation optimization issue, which concentrates on enriching the system sum-throughput, by realizing the transmitted power constraint and also fulfilling the minimum throughput for each user. However, to tackle this mentioned optimization problem, a Modified Artificial Bee Colony (MABC) algorithm is proposed. Besides, the designed MABC algorithm obtains optimal powers among multiplexed users on every sub-channel. Further, simulation results illustrate that the presented power allocation scheme-based NOMA system's sum throughput is higher than the original ABC-based power allocation and other state-of-the-art power allocation schemes. Moreover, the MABC method swiftly converges to optimal solutions compared to the original ABC algorithm under selected control parameters.

  1. ANDREWS, J. G., BUZZI, S., CHOI, W., et al. What will 5G be? IEEE Journal of Selected Areas in Communications, 2014, vol. 32, no. 6, p. 1065–1082. DOI: 10.1109/JSAC.2014.2328098
  2. ISLAM, S. M. R., AVAZOV, N., DOBRE, O. A., et al. Power-domain non-orthogonal multiple access (NOMA) in 5G systems: Potentials and challenges. IEEE Communications Surveys & Tutorials, 2017, vol. 19, no. 2, p. 721–742. DOI: 10.1109/COMST.2016.2621116
  3. GUPTA, A., JHA, R. K. A survey of 5G network: Architecture and emerging technologies. IEEE Access, 2015 vol. 3, p. 1206–1232. DOI: 10.1109/ACCESS.2015.2461602
  4. LIU, Y., QIU, Z., ELKASHLAN, M., et al. Non-orthogonal multiple access for 5G and beyond. Proceedings of IEEE, 2017, vol. 105, no. 12, p. 2347–2381. DOI: 10.1109/JPROC.2017.2768666
  5. DAVIS, K., BERNDT, H. 6G vision and requirements: is there any need for beyond 5G?. IEEE Vehicular Technology Magazine, 2018, vol. 13, no. 3, p. 72–80. DOI: 10.1109/MVT.2018.2848498
  6. ISLAM, S. M. R., ZENG, M., DOBRE, O. A., et al. Resource allocation for downlink NOMA systems: Key techniques and open issues. IEEE Wireless Communications, 2018, vol. 25, no. 2, p. 40–47. DOI: 10.1109/MWC.2018.1700099
  7. KALPANA, N., ALI KHAN, M. Z. Fast computation of generalized water-filling problems. IEEE Signal Processing Letters, 2015, vol. 22, no. 11, p. 1884–1887. DOI: 10.1109/LSP.2015.2440653
  8. KALPANA, N., ALI KHAN, M. Z. An efficient direct solution of cave-filling problems. IEEE Transactions on Communications, 2016, vol. 64, no. 7, p. 3064–3077. DOI: 10.1109/TCOMM.2016.2560813
  9. KALPANA, N., RAMESH, B. Quick resource allocation in heterogeneous networks. Wireless Networks, 2018, vol. 24, no. 8, p. 3171–3188. DOI: 10.5555/3287990.3288049
  10. KALPANA, N., RAMESH, B. Swift resource allocation in wireless networks. IEEE Transactions on Vehicular Technology, 2018, vol. 67, no. 7, p. 5965–5979. DOI: 10.1109/TVT.2018.2805938
  11. RAJESWARI, K., THIRUVENGADAM, S., J. Optimal power allocation for channel estimation in MIMO-OFDM system with per-subcarrier transmit antenna selection. Radioengineering, 2015, vol. 24, no. 1, p. 105–114. DOI: 10.13164/re.2015.0105
  12. KALPANA, N., PARCHURI, A. Efficient allotment of resources in heterogeneous communication. Wireless Networks, 2021, vol. 27, p. 3761–3783. DOI: 10.1007/s11276-021-02599-x
  13. KALPANA, N., SREENU, S. Remote health monitoring system using heterogeneous networks. Healthcare Technology Letters, 2021, vol. 9, no. 1–2, p. 1–9. DOI: 10.1049/htl2.12020
  14. BENJEBBOVU, A., LI, A., SAITO, Y., et al. System-level performance of downlink NOMA for future LTE enhancements. In IEEE GlobecomWork Shops (GC Wkshps). Atlanta (USA), 2013, p. 66–70. DOI: 10.1109/GLOCOMW.2013.6824963
  15. LEI, L., YUAN, D., HO, C. K., et al. Power and channel allocation for non-orthogonal multiple access in 5G systems: Tractability and computation. IEEE Transactions onWireless Communications, 2016, vol. 15, no. 12, p. 8580–8594. DOI: 10.1109/TWC.2016.2616310.
  16. DING, Z., YANG, Z., FAN, P., et al. On the performance of non-orthogonal multiple access in 5G systems with randomly deployed users. IEEE Signal Processing Letters, 2014, vol. 21, no. 12, p. 1501–1505. DOI: 10.1109/LSP.2014.2343971
  17. HUSSAIN, Q., SARMAD, S. Full duplex relaying in non-orthogonal multiple access system with advanced successive interference cancellation. Radioengineering, 2020, vol. 29, no. 4, p. 654–662. DOI: 10.13164/re.2020.0654
  18. SREENU, S., KALPANA,N.Novel user association scheme deployed for the downlink NOMA systems. In International Conference on Communication and Intelligent Systems. New Delhi (India), 2021.
  19. CHEN, W., ZHAO, S., ZHANG, R., et al. Generalized user grouping in NOMA based on overlapping coalition formation game. IEEE Journal on Selected Areas in Communications, 2021, vol. 39, no. 4, p. 969–981. DOI: 10.1109/JSAC.2020.3018832
  20. BENJEBBOUR, A., SAITO, Y., KISHIYAMA, Y., et al. Concept and practical considerations of non-orthogonal multiple access (NOMA) for future radio access. In International Symposium on Intelligent Signal Processing and Communication Systems. Naha (Japan), 2013, p. 770–774. DOI: 10.1109/ISPACS.2013.6704653
  21. HE, J., TANG, Z. Low-complexity user pairing and power allocation algorithm for 5G cellular network non-orthogonal multiple access. Electronics Letters, 2017, vol. 53, no. 9, p. 626–627. DOI: 10.1049/el.2016.4190
  22. BENJEBBOUR, A., LI, A., KISHIYAMA, Y., et al. Systemlevel performance of downlink NOMA combined with SUMIMO for future LTE enhancements. In IEEE Globecom Workshops (GC Wkshps). Austin (USA), 2014, p. 706–710. DOI: 10.1109/GLOCOMW.2014.7063515
  23. HOJEIJ, M., FARAH, J., NOUR, C. A., et al. Resource allocation in downlink non-orthogonal multiple access (NOMA) for future radio access. In IEEE 81st Vehicular Technology Conference (VTC Spring). Glasgow (UK), 2015, p. 1–6. DOI: 10.1109/VTCSpring.2015.7146056
  24. SARAEREH, O. A., ALSARAIRA, A., KHAN, I., et al. An efficient resource allocation algorithm for OFDM-based NOMA in 5G systems. Electronics Journal, 2019, vol. 8, no. 12, p. 1–13. DOI: 10.3390/electronics8121399
  25. SAITO, Y., KISHIYAMA, Y., BENJEBBOUR, A., et al. Non-orthogonal multiple access (NOMA) for cellular future radio access. In IEEE 77th Vehicular Technology Conference (VTC Spring). Dresden (Germany), 2013, p. 1–5. DOI: 10.1109/VTCSpring.2013.6692652
  26. PARIDA, P., DAS, S. S. Power allocation in OFDM based NOMA systems: A DC programming approach. In IEEE Globecom Workshops (GC Wkshps). Austin (USA), 2014, p. 1026–1031. DOI: 10.1109/GLOCOMW.2014.7063568
  27. OVIEDO, J. A., SADJADPOUR, H. R., A fair power allocation approach to NOMA in multiuser SISO systems. IEEE Transactions on Vehicular Technology, 2017, vol. 66, no. 9, p. 7974–7985. DOI: 10.1109/TVT.2017.2689000
  28. TANG, T., MAO, Y., HU, G. Fair power allocation approach to OFDM-based NOMA with consideration of clipping. Electronics Journal, 2020, vol. 9, no. 10, p. 1–12. DOI: 10.3390/electronics9101743
  29. SUN, Q., HAN, S., LI, C., et al. On the ergodic capacity of MIMO NOMA systems. IEEEWireless Communication Letters, 2015, vol. 4, no. 4, p. 405–408. DOI: 10.1109/LWC.2015.2426709
  30. ALI, Z. J., NOORDIN, N. K., SALI, A., et al. Novel resource allocation techniques for downlink non-orthogonal multiple access systems. Applied Sciences, 2020, vol. 10, no. 17, p. 1–19. DOI: 10.3390/app10175892
  31. MA, X., WU, J., ZHANG, Z., et al. Power allocation for downlink of non-orthogonal multiple access system via genetic algorithm. In International Conference on 5G for Future Wireless Networks. Beijing (China), 2017, p. 459–470. DOI: 10.1007/978-3-319-72823-0
  32. GUO, Y. X., HUI, L. A power allocation method based on particle swarm algorithm for NOMA downlink networks. Journal of Physics: Conference Series, 2018, vol 1087, no. 2, p. 1–7. DOI: 10.1088/1742-6596/1087/2/022033
  33. GOUDOS, S. Joint power allocation and user association in non-orthogonal multiple access networks: An evolutionary approach. Physical Communication, 2019, vol. 37, p. 1–7. DOI: 10.1016/j.phycom.2019.100841
  34. KARABOGA, D., AKAY, B. A comparative study of artificial Bee colony algorithm. Applied Mathematics and Computation, 2009, vol. 214, no. 1, p. 108–132. DOI: 10.1016/j.amc.2009.03.090
  35. KARABOGA, D., AKAY, B. A survey: Algorithms simulating bee swarm intelligence. Artificial Intelligence Review, 2009, vol. 31, no. 14, p. 61–85. DOI: 10.1007/s10462-009-9127-4
  36. CAO, Y., LU, Y., PAN, X., et al. An improved global best guided artificial bee colony algorithm for continuous optimization problems. Cluster Computing, 2019, vol. 22, no. 2, p. 3011–3019. DOI: 10.1007/s10586-018-1817-8
  37. SHAHAB, M. B., IRFAN, M., KADER, M. D., et al. User pairing schemes for capacity maximization in non-orthogonal multiple access systems. Wireless Communications and Mobile Computing, 2016, vol. 16, p. 2884–2894. DOI: 10.1002/wcm.2736
  38. FU, Y., HONG, Y., CHEN, L., et al. Enhanced power allocation for sum rate maximization in OFDM-NOMA VLC systems. IEEE Photonics Technology Letters, 2018, vol. 30, no. 13, p. 1218–1221. DOI: 10.1109/LPT.2018.2839094
  39. CETINKAYA, C., ARSLAN, H. Energy and spectral efficiency trade-off in NOMA: Multi-objective evolutionary approaches. In IEEE International Conference on Communications Workshops (ICC Workshops). Dublin (Ireland), 2020, p. 1–6. DOI: 10.1109/ICCWorkshops49005.2020.9145261
  40. KARABOGA, D. An Idea Based on Honey Bee Swarm for Numerical Optimization. TechnicalReport - TR06, ErciyesUniversity, Computer Engineering Department, Turkey, 2005.
  41. YENIAY, O. Penalty function methods for constrained optimization with genetic algorithms. Mathematical and Computational Applications, 2005, vol. 10, no. 1, p. 45–56. DOI: 10.3390/mca10010045
  42. GAO, W.-F., LIU S.-Y. A modified artificial bee colony algorithm. Computers and Operations Research, 2012, vol. 39, no. 3, p. 687–697. DOI: 10.1016/j.cor.2011.06.007
  43. PANG, B., SONG, Y., ZHANG, C., et al. A modified artificial bee colony algorithm based on the self-learning mechanism. Algorithms, 2018, vol. 11, no. 6, p. 1–21. DOI: 10.3390/a11060078

Keywords: NOMA, artificial bee colony, power allocation, sum rate

H. Rayat, R. Dastanian [references] [full-text] [DOI: 10.13164/re.2022.0323] [Download Citations]
A 2V, 32.13nA, fully MOSFET Voltage Limiter for Low Power Applications

This paper presents a fully MOSFET DC voltage limiter with low current consumption. In the proposed voltage reference structure to reduce power consumption, transistors are biased in the sub-threshold region. To generate complementary to absolute temperature (CTAT) voltage in the voltage reference circuit, only a PMOS transistor is used, in which its drain, gate, and source terminals are connected together and acts as a diode that reduces the layout area occupation. To further reduce power consumption, a part of the rectifier output voltage is compared with the reference voltage by the sampling circuit. Also, four stage inverters are used as buffers to provide the I-V limiting characteristic closer to the ideal situation. The use of series pass-gate transistors in the first inverter also reduces power consumption as much as possible. The results of post-layout simulation based on 0.18μm CMOS technology depict that the suggested voltage reference circuit has a reference voltage equivalent to 0.579V with a TC of 37.2ppm/℃ in the temperature range of -50°C to 50°C. LR and PSRR attained 0.008%/V and 45dB, respectively. The output voltage and current consumption of the limiter circuit are 2V and 32.13nA, respectively. The total layout area of the proposed limiter is 3249µm2.

  1. LIN, K., WANG, B., WANG, X., et al. A high-efficient energy harvest chain for ultra-low power passive UHF RFID tags. In 12th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT). Guilin (China), 2014, p. 1–3. DOI: 10.1109/ICSICT.2014.7021596
  2. FERNANDEZ, E., BERIAIN, A., SOLAR, H., et al. A low power voltage limiter for a full passive UHF RFID sensor on a 0.35 μm CMOS process. Microelectronics Journal, 2012, vol. 43, no. 10, p. 708–713. DOI: 10.1016/j.mejo.2012.03.013
  3. LI, S., SHEN, J., LIU, S., et al. A novel voltage limiter circuit for passive RFID tag. In IET International Conference on Information Science and Control Engineering (ICISCE). Shenzhen (China), 2012, p. 1–4. DOI: 10.1049/cp.2012.2400
  4. ZURIARRAIN, X., BERIAIN, A., BISTUE, G., et al. A CMOS low frequency analog RFID front-end for the IoT. In Conference on Design of Circuits and Integrated Systems (DCIS). Lyon (France), 2018, p. 1–6. DOI: 10.1109/DCIS.2018.8681486
  5. BERIAIN, A., REZOLA, A., DEL RIO, D. A high accuracy 3.1V voltage limiter for enabling high performance RFID sensor applications. In IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS). Dallas (TX, USA), 2019, p. 973 to 976. DOI: 10.1109/MWSCAS.2019.8885052
  6. LEE, J.-W., VO, D. H. T., HUYNH, Q.-H., et al. A fully integrated HF-band passive RFID tag IC using 0.18-um CMOS technology for low-cost security applications. IEEE Transactions on Industrial Electronics, 2011, vol. 58, no. 6, p. 2531–2540. DOI: 10.1109/TIE.2010.2060460
  7. CHUNG, C., KIM, Y.-H., KI, T.-H., et al. Fully integrated ultralow-power passive UHF RFID transponder IC. In IEEE International Symposium on Radio-Frequency Integration Technology (RFIT). Beijing (China), 2011, p. 77–80. DOI: 10.1109/RFIT.2011.6141789
  8. SALEHI, M. R., DASTANIAN, R., ABIRI, E., et al. A 1.58 nW power consumption and 34.45 ppm/°C temperature coefficient bandgap reference (BGR) for subblocks of RFID tag. Microelectronics Journal, 2015, vol. 46, no. 5, p. 383–389. DOI: 10.1016/j.mejo.2015.03.002
  9. NEJADHASAN, S., ZAHERI, F., ABIRI, E., et al. PVT-compensated low voltage LNA based on variable current source for low power applications. AEU - International Journal of Electronics and Communications, 2022, vol. 143, no. 5, p. 1–17. DOI: 10.1016/j.aeue.2021.154042
  10. WANG, L., ZHAN, C., TANG, J., et al. A 0.9-V 33.7-ppm/°C 85-nW sub-bandgap voltage reference consisting of subthreshold MOSFETs and single BJT. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2018, vol. 26, no. 10, p. 2190–2194. DOI: 10.1109/TVLSI.2018.2836331
  11. PEREIRA-RIAL, O., CARRILLO, J. M., LOPEZ, P., et al. A 0.6 V, ultra-low power, 1060 μm2 self-biased PTAT voltage generator for implantable biomedical devices. AEU - International Journal of Electronics and Communications, 2021, vol. 137, no. 12, p. 1–8. DOI: 10.1016/j.aeue.2021.153800
  12. WANG, L., ZHAN, C. A 0.7-V 28-nW CMOS subthreshold voltage and current reference in one simple circuit. IEEE Transactions on Circuits and Systems I: Regular Papers, 2019, vol. 66, no. 9, p. 3457–3466. DOI: 10.1109/TCSI.2019.2927240
  13. PARK, M., PARK, S. M. A CMOS symmetric self-biased voltage reference. Microelectronics Journal, 2018, vol. 80, no. 11, p. 28–33. DOI: 10.1016/j.mejo.2018.08.002
  14. HUANG, C., ZHAN, C., HE, L., et al. A 0.6-V minimum-supply, 23.5 ppm/°C subthreshold CMOS voltage reference with 0.45% variation coefficient. IEEE Transactions on Circuits and Systems II: Express Briefs, 2018, vol. 65, no. 10, p. 1290–1294. DOI: 10.1109/TCSII.2018.2846808
  15. AMINZADEH, H., VALINEZHAD, M. M. 0.7-V supply, 21-nW all–MOS voltage reference using a MOS-only current-driven reference core in digital CMOS. Microelectronics Journal, 2020, vol. 102, no. 7, p. 1–10. DOI: 10.1016/j.mejo.2020.104841
  16. ABDOLLAHI, R., BADFAR, E. Harmonics mitigation approach by using pulse-doubling circuit in the AC-DC rectifier. International Journal of Circuit Theory and Applications, 2021, vol. 49, no. 8, p. 2436–2452. DOI: 10.1002/cta.2991
  17. SALEHI, M. R., DASTANIAN, R., ABIRI, E., et al. A 147 μW, 0.8 V and 7.5 (mV/V) LIR regulator for UHF RFID application. AEU - International Journal of Electronics and Communications, 2015, vol. 69, no. 1, p. 133–140. DOI: 10.1016/j.aeue.2014.08.004
  18. SHRIVASTAVA, A., CRAIG, K., ROBERTS, N. E., et al. A 32nW bandgap reference voltage operational from 0.5V supply for ultra-low power systems. In IEEE International Solid-State Circuits Conference - (ISSCC) Digest of Technical Papers. San Francisco (CA, USA), 2015, p. 94–95. DOI: 10.1109/ISSCC.2015.7062942
  19. RAYAT, H., DASTANIAN, R. A fully MOSFET voltage reference with low power consumption and high power supply rejection ratio for IoT microsystems. Jordan Journal of Electrical Engineering, 2022, vol. 8, no. 2, p. 102–113. DOI: 10.5455/jjee.204-1642106587

Keywords: Limiter, voltage reference, temperature coefficient, low power, voltage sampler, OPA, buffer

X. Wang, B. Jin, L. Huang, M. Zhang, M. Fang [references] [full-text] [DOI: 10.13164/re.2022.0331] [Download Citations]
A Novel High-Sensitivity Broadband Rectifier for Ambient RF Energy Harvesting

In this paper, a novel high-sensitivity broadband rectifier is proposed aiming at ambient radio frequency (RF) energy harvesting. Traditionally, voltage doubling rectifying circuit is used to design high-sensitivity rectifier. But when the input power is lower, the rectifying efficiency is significantly reduced. Therefore, a improved parallel half-wave rectifying circuit is proposed in this article which can convert RF energy in the whole period. And the proposed rectifying circuit can work better in lower power environment and has a higher efficiency level. Besides, the impedance match is also important component of rectifier. Due to the nonlinearity and complexity of rectifying circuit, achieving wideband matching network is a challenge. Thus, a design approach of broadband impedance circuit is given in this study. Combining with the proposed high-sensitivity rectifying circuit, a high-sensitivity wideband rectifier can be generated, when the input power is -15dBm, -20dBm, -25dBm, the efficiency is 43%, 32%, 20%, respectively. Finally, a second-order wideband rectifier with high sensitivity is realized, and the range of bandwidth can cover four main frequency bands of GSM 900 MHz, GSM 1800 MHz, UMTS 2100 MHz, WLAN 2400 MHz. To verify the validity, the rectifier is fabricated and measured, and the measurement has a good agreement with simulation results.

  1. PINUELA, M., MITCHESON, P. D., LUCYSZYN, S. Ambient RF energy harvesting in urban and semi-urban environments. IEEE Transactions on Microwave Theory and Techniques, 2013, vol. 61, no. 7, p. 2715–2726. DOI: 10.1109/TMTT.2013.2262687
  2. TAVARES, J., BARROCA, N., SARAIVA, H. M., et al. Spectrum opportunities for electromagnetic energy harvesting from 350 MHz to 3 GHz. In 2013 7th International Symposium on Medical Information and Communication Technology (ISMICT). Tokyo (Japan), 2013, p. 126–130. DOI: 10.1109/ISMICT.2013.6521714
  3. SONG, C., HUANG, Y., ZHOU, J., et al. A broadband efficient rectenna array for wireless energy harvesting. In 2015 9th European Conference on Antennas and Propagation (EuCAP). Lisbon (Portugal), 2015, p. 1–5. ISSN: 2164-3342
  4. MANSOUR, M. M., KANAYA, H. Compact RF rectifier circuit for ambient energy harvesting. In 2017 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT). Seoul (South Korea), 2017, p. 220–222. DOI: 10.1109/RFIT.2017.8048256
  5. HUANG, Y., SHINOHARA, N., TOROMURA, H. A wideband rectenna for 2.4 GHz-band RF energy harvesting. In 2016 IEEE Wireless Power Transfer Conference (WPTC). Aveiro (Portugal), 2016, p. 1–3. DOI: 10.1109/WPT.2016.7498816
  6. OLGUN, U., CHEN, C. C., VOLAKIS, J. L. Low-profile planar rectenna for batteryless RFID sensors. In 2010 IEEE Antennas and Propagation Society International Symposium. Toronto (Canada), 2010, p. 1–4. DOI: 10.1109/APS.2010.5562220
  7. REN, Y. J., CHANG, K. 5.8-GHz circularly polarized dual-diode rectenna and rectenna array for microwave power transmission. IEEE Transactions on Microwave Theory and Techniques, 2006, vol. 54, no. 4, p. 1495–1502. DOI: 10.1109/TMTT.2006.871362
  8. FARINHOLT, K. M., PARK, G., FARRAR, C. R. RF energy transmission for a low-power wireless impedance sensor node. IEEE Sensors Journal, 2009, vol. 9, no. 7, p. 793–800. DOI: 10.1109/JSEN.2009.2022536
  9. KHANSALEE, E., ZHAO, Y., LEELARASMEE, E., et al. A dualband rectifier for RF energy harvesting systems. In 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). Nakhon Ratchasima (Thailand), 2014, p. 1–4. DOI: 10.1109/ECTICon.2014.6839870
  10. KUHN, V., LAHUEC, C., SEGUIN, F., et al. A multi-band stacked RF energy harvester with RF-to-DC efficiency up to 84%. IEEE Transactions on Microwave Theory and Techniques, 2015, vol. 63, no. 5, p. 1768–1778. DOI: 10.1109/TMTT.2015.2416233
  11. VU, H. S., NGUYEN, N., HA-VAN, N., et al. Multiband ambient RF energy harvesting for autonomous IoT devices. IEEE Microwave and Wireless Components Letters, 2020, vol. 30, no. 12, p. 1189–1192. DOI: 10.1109/LMWC.2020.3029869
  12. TAFEKIRT, H., PELEGRI-SEBASTIA, J., BOUAJAJ, A., et al. A sensitive triple-band rectifier for energy harvesting applications. IEEE Access, 2020, vol. 8, p. 73659–73664. DOI: 10.1109/ACCESS.2020.2986797
  13. SONG, C., HUANG, Y., CARTER, P., et al. A novel six-band dual CP rectenna using improved impedance matching technique for ambient RF energy harvesting. IEEE Transactions on Antennas and Propagation, 2016, vol. 64, no. 7, p. 3160–3171. DOI: 10.1109/TAP.2016.2565697
  14. LIU, Z., ZHONG, Z., GUO, Y. X. Enhanced dual-band ambient RF energy harvesting with ultra-wide power range. IEEE Microwave and Wireless Components Letters, 2015, vol. 25, no. 9, p. 630–632. DOI: 10.1109/LMWC.2015.2451397
  15. MOULAY, A., DJERAFI, T. Multi-stage Schottky diode power harvester for UWB application. In 2018 IEEE Wireless Power Transfer Conference (WPTC). Montreal (QC, Canada), 2018, p. 115–119. DOI: 10.1109/WPT.2018.8639080
  16. SONG, C., HUANG, Y., ZHOU, J., et al. Improved ultrawideband rectennas using hybrid resistance compression technique. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 4, p. 2057–2062. DOI: 10.1109/TAP.2017.2670359
  17. LIU, W., HUANG, K., WANG, T., et al. A broadband highefficiency RF rectifier for ambient RF energy harvesting. IEEE Microwave and Wireless Components Letters, 2020, vol. 30, no. 12, p. 1185–1188. DOI: 10.1109/LMWC.2020.3028607
  18. SONG, C., HUANG, Y., J. ZHOU, J., et al. A high-efficiency broadband rectenna for ambient wireless energy harvesting. IEEE Transactions on Antennas and Propagation, 2015, vol. 63, no. 8, p. 3486–3495. DOI: 10.1109/TAP.2015.2431719
  19. PALAZZI, V., HESTER, J., BITO, J., et al. A novel ultra-lightweight multiband rectenna on paper for RF energy harvesting in the next generation LTE bands. IEEE Transactions on Microwave Theory and Techniques, 2017, vol. 66, no. 1, p. 366–379. DOI: 10.1109/TMTT.2017.2721399
  20. WU, Y., LIU, Y., LI, S. A dual-frequency transformer for complex impedances with two unequal sections. IEEE Microwave and Wireless Components Letters, 2009, vol. 19, no. 2, p. 77–79. DOI: 10.1109/LMWC.2008.2011315
  21. CHOW, Y. L., WAN, K. L. A transformer of one-third wavelength in two sections - for a frequency and its first harmonic. IEEE Microwave and Wireless Components Letters, 2002, vol. 12, no. 1, p. 22–23. DOI: 10.1109/7260.975723
  22. MONZON, C. Analytical derivation of a two-section impedance transformer for a frequency and its first harmonic. IEEE Microwave and Wireless Components Letters, 2002, vol. 12, no. 10, p. 381–382. DOI: 10.1109/LMWC.2002.804558
  23. MONZON, C. A small dual-frequency transformer in two sections. IEEE Transactions on Microwave Theory and Techniques, 2003, vol. 51, no. 4, p. 1157–1161. DOI: 10.1109/TMTT.2003.809675
  24. MILLIGAN, T. A. Transmission-line transformation between arbitrary impedances (Letters). IEEE Transactions on Microwave Theory and Techniques, 1976, vol. 24, no. 3, p. 159–159. DOI: 10.1109/TMTT.1976.1128802
  25. POTOK, M. H. N. Comments on "Transmission-line transformation between arbitrary impedances" (Letters). IEEE Transactions on Microwave Theory and Techniques, 1977, vol. 25, no. 1, p. 77–77. DOI: 10.1109/TMTT.1977.1129040
  26. HUANG, L., LI, M., ZHANG, P., et al. A novel miniaturized UWB bandpass filter basing on E-shaped defected microstrip structure. Progress In Electromagnetics Research Letters, 2020, vol. 93, p. 49–57. DOI: 10.2528/PIERL20062601

Keywords: High-sensitivity, broadband rectifier, RF energy harvesting

X. W. Dai, D. L. Mi, H. T. Wu, Y. H. Zhang [references] [full-text] [DOI: 10.13164/re.2022.0339] [Download Citations]
Design of Compact Patch Antenna Based on Support Vector Regression

In this paper, support vector regression (SVR) algorithm is used for compact patch antenna design. By etching three T-shaped slots on the ground plane of a rectangle patch antenna, the current distribution on the ground plane is changed and the resonant frequency is reduced. However, there is no reliable formula between the physical parameters of slots and the resonant frequency for antenna design. In this paper, the SVR algorithm is innovatively used to establish the mapping relationship between four parameters and the resonant frequency. In order to reduce the data samples required to train the SVR model, these four parameters are divided into three groups. This grouping method ensures the reasonable distribution of data samples, and greatly reduces the training data samples and reduces the time to collect data by simulator software. The hyperparameters are optimized by using 10-fold cross validation. 108 antenna models (data samples) with different geometrical and electrical parameters are designed and simulated for the initial dataset. The SVR model is trained on the 75 data samples with the coefficient of determination (R2) of 0.9736 and is tested on the remainder 33 data samples. With the computation of the SVR model, the size of the proposed antenna decreases by 19.18% compared with that of the conventional rectangle patch antenna. The proposed structure is fabricated and measured. The results show that the proposed SVR model has good generalization on the real antenna model.

  1. DAI, X. W., MI, D. L., HONG, H., et al. Dual-polarized antenna with suppression of cross-band scattering in multiband array. IEEE Antennas and Wireless Propagation Letters, 2021, vol. 20, no. 8, p. 1592–1595. DOI: 10.1109/LAWP.2021.3091650
  2. JANG, T. H., KIM, H. Y., SONG, I. S., et al. A wideband aperture efficient 60-GHz series-fed E-shaped patch antenna array with copolarized parasitic patches. IEEE Transactions on Antennas and Propagation, 2016, vol. 64, no. 12, p. 5518–5521. DOI: 10.1109/TAP.2016.2621023
  3. GE, Y., ESSELLE, K. P., BIRD, T. S. E-shaped patch antennas for high-speed wireless networks. IEEE Transactions on Antennas and Propagation, 2004, vol. 52, no. 12, p. 3213–3219. DOI: 10.1109/TAP.2004.836412
  4. LIU, Q., LU, Y. CPW-fed wearable textile L-shape patch antenna. In Proceedings of 2014 3rd Asia-Pacific Conference on Antennas and Propagation. Harbin (China), 2014, p. 461–462. DOI: 10.1109/APCAP.2014.6992526
  5. WONG, K. L., KUO, J. S., CHIOU, T. W. Compact microstrip antennas with slots loaded in the ground plane. In 2001 Eleventh International Conference on Antennas and Propagation, (IEE Conf. Publ. No. 480). Manchester (UK), 2001, vol. 2, p. 623–626. DOI: 10.1049/cp:20010364
  6. QIAN, B., HUANG, X., CHEN, X., et al. Surrogate-assisted defected ground structure design for reducing mutual coupling in 2×2 microstrip antenna array. IEEE Antennas and Wireless Propagation Letters, 2022, vol. 21, no. 2, p. 351–355. DOI: 10.1109/LAWP.2021.3131600
  7. WU, Q., CAO, Y., WANG, H., et al. Machine-learning-assisted optimization and its application to antenna designs: Opportunities and challenges. China Communications, 2020, vol. 17, no. 4, p. 152–164. DOI: 10.23919/JCC.2020.04.014
  8. DAVLI, A., GUERZONI, G., VITETTA, G. M. Machine learning and deep learning techniques for colocated MIMO radars: A tutorial overview. IEEE Access, 2021, vol. 9, p. 33704–33755. DOI: 10.1109/ACCESS.2021.3061424
  9. CUI, L., ZHANG, Y., ZHANG, R., et al. A modified efficient KNN method for antenna optimization and design. IEEE Transactions on Antennas and Propagation, 2020, vol. 68, no. 10, p. 6858–6866. DOI: 10.1109/TAP.2020.3001743
  10. CHEN, Y., ZHU, J., XIE, Y., et al. Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network. Nanoscale, 2019, vol. 11, no. 19, p. 9749–9755. DOI: 10.1039/c9nr01315f
  11. CAO, Y., WANG, G., ZHANG, Q. A new training approach for parametric modeling of microwave passive components using combined neural networks and transfer functions. IEEE Transactions on Microwave Theory and Techniques, 2009, vol. 57, no. 11, p. 2727–2742. DOI: 10.1109/TMTT.2009.2032476
  12. PRADO, D. R., LOPEZ-FERNANDEZ, J. A., ARREBOLA, M., et al. On the use of the angle of incidence in support vector regression surrogate models for practical reflectarray design. IEEE Transactions on Antennas and Propagation, 2021, vol. 69, no. 3, p. 1787–1792. DOI: 10.1109/TAP.2020.3015707
  13. YIĞIT, M. E., GUNEL G. O., GUNEL, T. SVR based design of triple band rectangular microstrip antenna for WLAN and 5G applications. In 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT). Alkhobar (Saudi Arabia), 2021, p. 1–5. DOI: 10.1109/ISAECT53699.2021.9668560
  14. ZHANG, J., AKINSOLU, M. O., LIU, B., et al. Design of zero clearance SIW endfire antenna array using machine learningassisted optimization. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 5, p. 3858–3863. DOI: 10.1109/TAP.2021.3137500
  15. USTUN, D., TOKTAS, A., AKDAGLI, A. Deep neural networkbased soft computing the resonant frequency of E-shaped patch antennas. International Journal of Electronics and Communications, 2019, vol. 102, p 54–61. DOI: 10.1016/j.aeue.2019.02.011
  16. SHARMA, Y., ZHANG, H. H., XIN, H. Machine learning techniques for optimizing design of double T-shaped monopole antenna. IEEE Transactions on Antennas and Propagation, 2020, vol. 68, no. 7, p. 5658–5663. DOI: 10.1109/TAP.2020.2966051
  17. ULKER, S. Support vector regression analysis for the design of feed in a rectangular patch antenna. In 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). Ankara (Turkey), 2019, p. 1–3. DOI: 10.1109/ISMSIT.2019.8932929
  18. PRADO, D. R., LOPEZ-FERNANDEZ, J. A., ARREBOLA, M., et al. Support vector regression to accelerate design and crosspolar - optimization of shaped-beam reflectarray antennas for space applications. IEEE Transactions on Antennas and Propagation, 2019, vol. 67, no. 3, p. 1659–1668. DOI: 10.1109/TAP.2018.2889029

Keywords: Support Vector Regression (SVR), compact design, patch antenna, T-shaped slots

L. Ge, C. Qi, Y. Guo, L. Qian, J. Tong, P. Wei [references] [full-text] [DOI: 10.13164/re.2022.0346] [Download Citations]
Classification Weighted Deep Neural Network Based Channel Equalization for Massive MIMO-OFDM Systems

Massive multi-input multi-output (MIMO) has attracted significant interest in academia and industry, which can efficiently increase the transmission rate. However, the error rate of conventional channel equalizations in massive MIMO systems may be high owing to the dynamic channel states in practical conditions. To solve this problem, in this paper, we propose an improved channel equalization framework based on the deep neural network (DNN). Based on the analyzed relationship between the input and output of the DNN, the data can be recovered without the channel state information. Furthermore, aiming at reducing the convergence time and enhancing the learning ability of the DNN, a classification weighted algorithm is proposed to optimize the cost function of the DNN, which is named as classification weighted deep neural network (CW-DNN). Simulation results demonstrate that compared to conventional counterparts, the proposed CW-DNN based equalizer can achieve a better normalized mean square error (NMSE). Upon approximating the optimal neural network parameters with the significantly improved convergence speed and reduced training time of the network, under the condition of the fixed learning rate.

  1. GE, L., ZHANG, Y., CHEN, G., et al. Compression-based LMMSE channel estimation with adaptive sparsity for massive MIMO in 5G systems. IEEE Systems Journal, 2019, vol. 13, no. 4, p. 3847–3857. DOI: 10.1109/JSYST.2019.2897862
  2. GE, L., GOU, Y., ZHANG, Y., et al. Deep neural network based channel estimation for massive MIMO-OFDM systems with imperfect channel state information. IEEE Systems Journal, 2021, p. 1–11. DOI: 10.1109/JSYST.2021.3114229
  3. MARZETTA, T. L. Non-cooperative cellular wireless with unlimited numbers of base station antennas. IEEE Transactions on Wireless Communications, 2010, vol. 9, no. 11, p. 3590–3600. DOI: 10.1109/TWC.2010.092810.091092
  4. LUO, H., ZHANG, Y., HUANG, L., et al. A closed-loop reciprocity calibration method for massive MIMO in terrestrial broadcasting systems. IEEE Transactions on Broadcasting, 2017, vol. 63, no. 1, p. 11–19. DOI: 10.1109/TBC.2016.2606890
  5. ATAPATTU, L., ARACHCHIGE, G. M., ZIRI-CASTRO, K., et al. Linear adaptive channel equalization for multiuser MIMO-OFDM systems. In Australasian Telecommunication Networks and Applications Conference (ATNAC). Brisbane (Australia), 2012, p. 1–5. DOI: 10.1109/ATNAC.2012.6398080
  6. HIGUCHI, K., KAWAI, H., MAEDA, N., et al. Likelihood function for QRM-MLD suitable for soft-decision turbo decoding and its performance for OFCDM multiplexing in multipath fading channel. In IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). Barcelona (Spain), 2004, p. 1142–1148. DOI: 10.1109/PIMRC.2004.1373877
  7. SHIN, W., KIM, H., SON, M., et al. An improved LLR computation for QRM-MLD in coded MIMO systems. In IEEE 66th Vehicular Technology Conference. Baltimore (USA), 2007, p. 447–451. DOI: 10.1109/VETECF.2007.105
  8. YANG, C. Y., CHEN, B. S. Adaptive channel-tracking method and equalization for MC-CDMA systems over rapidly fading channel under colored noise. EURASIP Journal on Advances in Signal Processing, 2010, article no. 871250, p. 1–12. DOI:10.1155/2010/871650
  9. ARABLOUEI, R., DOGˆ ANÇAY K. Low-complexity adaptive decision-feedback equalization of MIMO channels. Signal Processing, 2012, vol. 92, no. 6, p. 1515–1524. DOI: 10.1016/j.sigpro.2011.12.012
  10. CHEN, S., ZHU C. ICI and ISI analysis and mitigation for OFDM systems with insufficient cyclic prefix in time-varying channels. IEEE Transactions on Consumer Electronics, 2004, vol. 50, no. 1, p. 78–83. DOI: 10.1109/TCE.2004.1355879
  11. YANG, L., NIE, M., ZHONG, Z. Channel equalization of MIMO-OFDM system based on extreme learning machine. Applied Mechanics and Materials, 2014, vol. 536–537, p. 1751–1757. DOI:10.4028/
  12. NAWAZ, S. J., MOHSIN, S., IKARAM, A. A. Neural network based MIMO-OFDM channel equalizer using comb-type pilot arrangement. In International Conference on Future Computer and Communication. Kuala Lumpar (Malaysia), 2009, p. 36–41. DOI: 10.1109/ICFCC.2009.136
  13. BURSE, K.,YADAV, R.N., SHRIVASTAVA, S. C. Channel equalization using neural networks: A review. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2010, vol. 40, no. 3, p. 352–357. DOI: 10.1109/TSMCC.2009.2038279
  14. HAO, J., YANG, L. Semi-blind channel estimation of MIMO-OFDM systems based on RBF network. In IET International Communication Conference onWireless Mobile and Computing (CCWMC 2011). Shanghai (China), 2011, p. 187–191. DOI: 10.1049/cp.2011.0872
  15. HA, C. B., YOU, Y. H., SONG, H. K. Machine learning model for adaptive modulation of multi-stream in MIMOOFDM system. IEEE Access, 2018, vol. 7, p. 5141–5152. DOI: 10.1109/ACCESS.2018.2889076
  16. SIMSIR, S., TASPINAR,N. Channel estimation using neural network in orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) system. In International Telecommunications Symposium (ITS). Sao Paulo (Brazil), 2014, p. 1–5. DOI: 10.1109/ITS.2014.6947977
  17. TASPINAR, N., SEYMAN, M. N. Back propagation neural network approach for channel estimation in OFDM system. In IEEE International Conference on Wireless Communications, Networking and Information Security. Beijing (China), 2010, p. 265–268. DOI: 10.1109/WCINS.2010.5541934
  18. BOCCARDI, F., HEATH, R. W., LOZANO, A., et al. Five disruptive technology directions for 5G. IEEE Communications Magazine, 2014, vol. 52, no. 2, p. 74–80. DOI: 10.1109/MCOM.2014.6736746
  19. SEYMAN, M. N., TASPINAR, N. Channel estimation based on neural network in space time block coded MIMO-OFDM system. Digital Signal Processing, 2013, vol. 23, no. 1, p. 275–280. DOI:10.1016/j.dsp.2012.08.003
  20. WANG, T., WEN, C., WANG, H., et al. Deep learning for wireless physical layer: Opportunities and challenges. China Communications, 2017, vol. 14, no. 11, p. 92–111. DOI: 10.1109/CC.2017.8233654
  21. YE, H., LI, G. Y., TUANG, B. H. Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wireless Communications Letters, 2018, vol. 7, no. 1, p. 114–117. DOI: 10.1109/LWC.2017.2757490
  22. WANG, X., HAU, H., XU, Y. Pilot-assisted channel estimation and signal detection in uplink multi-user MIMO systems with deep learning. IEEE Access, 2020, vol. 8, p. 44936–44946. DOI: 10.1109/ACCESS.2020.2978253
  23. SAMUEL, N., DISKIN, T., WIESEL, A. Learning to Detect. IEEE Transactions on Signal Processing, 2019, vol. 67, no. 10, p. 2554–2564. DOI: 10.1109/TSP.2019.2899805
  24. BAEK, M., KWAK, S., JUNG, J., et al. Implementation methodologies of deep learning-based signal detection for conventional MIMO transmitters. IEEE Transactions on Broadcasting, 2019, vol. 65, no. 3, p. 636–642. DOI: 10.1109/TBC.2019.2891051
  25. HUANG, Q., ZHAO, C., JIANG, M., et al. A novel OFDM equalizer for large doppler shift channel through deep learning. In IEEE 90th Vehicular Technology Conference (VTC2019-Fall). Honolulu (USA), 2019, p. 1–5. DOI: 10.1109/VTCFall.2019.8891326
  26. LI, Q., ZHANG, A., LI J., et al. Soft decision signal detection of MIMO system based on deep neural network. In 5th International Conference on Computer and Communication Systems (ICCCS). Shanghai (China), 2020, p. 665–669. DOI: 10.1109/ICCCS49078.2020.9118425
  27. HE, H., WEN, C., JIN, S., et al. Model-driven deep learning for MIMO detection. IEEE Transactions on Signal Processing, 2020, vol. 68, p. 1702–1715. DOI: 10.1109/TSP.2020.2976585
  28. TAN, X., XU, W., SUN, K., et al. Improving massive MIMO message passing detectors with deep neural network. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 2, p. 1267–1280. DOI: 10.1109/TVT.2019.2960763
  29. LIAO, J., ZHAO, J., GAO, F., et al. A Model-driven deep learning method for massive MIMO detection. IEEE Communications Letters, 2020, vol. 24, no. 8, p. 1724–1728. DOI: 10.1109/LCOMM.2020.2989672
  30. XUE, S., MA, Y., LI, A., et al. On unsupervised deep learning solutions for coherentMU-SIMOdetection in fading channels. In IEEE International Conference on Communications (ICC). Shanghai (China), 2019, p. 1–6. DOI: 10.1109/ICC.2019.8761999
  31. CHENG, X., LIU, D., ZHU, Z., et al. A ResNet-DNN based channel estimation and equalization scheme in FBMC/OQAM systems. In 10th International Conference on Wireless Communications and Signal Processing (WCSP). Hangzhou (China), 2018, p. 1–5. DOI: 10.1109/WCSP.2018.8555649
  32. MIAO, P., YIN, W., PENG, H., et al. Deep learning based nonlinear equalization for DCO-OFDM systems. In IEEE International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT). Qingdao (China), 2021, p. 699–703, DOI: 10.1109/ICEEMT52412.2021.9602129

Keywords: Channel equalization, classification weighted, deep neural network, massive MIMO, optimization algorithm

M. M. Shaheen, N. M. Mahmoud, M. A. Ali, M. E. Nasr, A. H. Hussein [references] [full-text] [DOI: 10.13164/re.2022.0357] [Download Citations]
Implementation of a Highly Selective Microstrip Diplexer with Low Insertion Loss Using Square Open-Loop Resonators and a T-Junction Combiner

In this paper, the design and hardware implementation of a squared open-loop resonator (SOLR)-based microstrip diplexer with high isolation, low insertion loss, and high selectivity are introduced. We employed four SOLRs, with each pair of coupled SOLRs used to build a high selectivity bandpass filter (BPF). To assemble the proposed diplexer, the designed BPFs are linked together via a T-junction combiner that is matched to the two filters and the antenna port. For transmit and receive modes, the proposed diplexer has two resonance frequencies of ft = 1.81 GHz and fr = 2.03 GHz, respectively achieving a small frequency space ratio of R = 0.114. The simulated structure exhibits good insertion losses of about 1.98 dB and 1.9 dB for the two channels, respectively, with fractional bandwidths of 2.25% at 1.81 GHz and 3% at 2.03 GHz. For 1.81 GHz and 2.03 GHz, the simulated isolation values are 58 dB and 46 dB, respectively. While the fabricated structure exhibits better insertion losses of about 1.25 dB and 1.22 dB at the measured transmit and receive frequencies of 1.801 GHz and 2.001 GHz, respectively, with smaller fractional bandwidths of 2.23% at 1.801 GHz and 2.98% at 2.001 GHz. For 1.801 GHz and 2.001 GHz, the measured isolation values are 48.99 dB and 57.02 dB, respectively.

  1. HONG, J. S., LANCASTER, M. J. Coupling of microstrip square open-loop resonators for cross coupled planar microwave filters. IEEE Transactions on Microwave Theory and Techniques, 1996, vol. 48, no. 12, p. 2099–2109. DOI: 10.1109/22.543968
  2. KONPANG, J. A compact diplexer using square open loop with stepped impedance resonators. In IEEE Radio and Wireless Symposium. San Diego (CA, USA), 2009, p. 91–94. DOI: 10.1109/RWS.2009.4957292
  3. YANG, T., CHI, P., ITOH, T. High isolation and compact diplexer using the hybrid resonators. IEEE Microwave and Wireless Components Letters, 2010, vol. 20, no. 10, p. 551–553. DOI: 10.1109/LMWC.2010.2052793
  4. TIZYI, H., RIOUCH, F., TRIBAK, A., et al. Microstrip diplexer design based on two square open loop bandpass filters for RFID applications. International Journal of Microwave and Wireless Technologies, 2018, vol. 10, no. 4, p. 412–421. DOI: 10.1017/S1759078718000314
  5. NWAJANA, A. O., YEO, K. S. K. Microwave diplexer purely based on direct synchronous and asynchronous coupling. Radioengineering, 2016, vol. 25, no. 1, p. 247–252. DOI: 10.13164/re.2016.0247
  6. FENG, W., HONG, M., CHE, W. Microstrip diplexer design using open/shorted coupled lines. Progress In Electromagnetics Research Letters, 2016, vol. 59, p. 123–127. DOI: 10.2528/PIERL16030805
  7. HUSSEIN, A. H., ABDULLAH, H. H., ATTIA, M. A., et al. S-band compact microstrip full duplex Tx/Rx patch antenna with high isolation. IEEE Antennas and Wireless Propagation Letters, 2019, vol. 12, no. 10, p. 2090–2094. DOI: 10.1109/LAWP.2019.2937769
  8. CHENG, F., GU, C., ZHANG, B., et al. High isolation substrate integrated waveguide diplexer with flexible transmission zeros. IEEE Microwave and Wireless Components Letters, 2020, vol. 30, no. 11, p. 1029–1032.
  9. FENG, W., GAO, X., CHE, W. Microstrip diplexer for GSM and WLAN bands using common shorted stubs. Electronics Letters, 2014, vol. 50, no. 20, p. 1486–1488. DOI: 10.1049/el.2014.2500
  10. CHEN, C. F., HUANG, T. Y., CHOU, C. P., et al. Microstrip diplexers design with common resonator sections for compact size, but high isolation. IEEE Transactions on Microwave Theory and Techniques, 2006, vol. 54, no. 5, p. 1945–1952. DOI: 10.1109/TMTT.2006.873613
  11. SRISATHIT, S., PATISANG, S., PHROMLOUNGSRI, R., et al. High isolation and compact size microstrip hairpin diplexer. IEEE Microwave and Wireless Components Letters, 2005, vol. 15, no. 2, p. 101–103. DOI: 10.1109/LMWC.2004.842839

Keywords: Band-pass filter (BPF), microstrip diplexer, square open loop resonator (SOLR), T-junction combiner

C. L. Zhao, F. F. Yang, D. K. Waweru, C. Chen, H. J. Xu [references] [full-text] [DOI: 10.13164/re.2022.0362] [Download Citations]
Distributed QC-LDPC Coded Spatial Modulation for Half-Duplex Wireless Communications

The bit error rate (BER) performance of spatial modulation (SM) can be further improved by applying quasi-cyclic low-density parity-check (QC-LDPC) codes recommended in 5G to SM. It motivates us to propose a QC-LDPC coded SM (QC-LDPCC-SM) scheme, where SM signals are protected by QC-LDPC codes. To estimate the channel state information at the receiver, a novel iterative joint channel estimation and data detection based on variable block length (IJCEDD-VBL) for SM is presented. In standard 5G LDPC codes, the parity-check matrix contains multiple submatrices, and then we can construct two different QC-LDPC codes by suitably selecting the submatrices. Thus, the QC-LDPCC-SM scheme can be effectively extended to cooperative scenarios when deploying the generated LDPC codes at the source and relay, respectively. We develop an analytical approach for the BER performance of the proposed schemes. The simulation and theoretical results are in good agreement at high signal-to-noise ratio (SNR). Furthermore, the proposed coded cooperative scheme outperforms its corresponding non-cooperative counterpart and the existing scheme. The numerical results also validate the effectiveness of the proposed channel estimation scheme.

  1. NGUYEN, T. T. B., TAN, T. N., LEE, H. Efficient QC-LDPC encoder for 5G new radio. Electronics, 2019, vol. 8, no. 6, p. 1–15. DOI: 10.3390/electronics8060668
  2. LI, H., BAI, B., MU, X., et al. Algebra-assisted construction of quasi-cyclic LDPC codes for 5G new radio. IEEE Access, 2018, vol. 6, p. 50229–50244. DOI: 10.1109/ACCESS.2018.2868963
  3. NGUYEN, T. T. B., LEE, H. Low-complexity multi-mode multiway split-row layered LDPC decoder for gigabit wireless communications. Integration-the VLSI Journal, 2019, vol. 65, p. 189–200. DOI: 10.1016/j.vlsi.2018.12.004
  4. UMAR, R., YANG, F. F., XU, H. J., et al. Multi-level construction of polar coded single carrier-FDMA based on MIMO antennas for coded cooperative wireless communication. IET Communications, 2018, vol. 12, no. 10, p. 1253–1262. DOI: 10.1049/ietcom.2017.1436
  5. EJAZ, S., YANG, F., XU, H. Split labeling diversity for wireless half-duplex relay assisted cooperative communication systems. Telecommunication Systems, 2020, vol. 75, no. 4, p. 437–446. DOI: 10.1007/s11235-020-00694-6
  6. HUNTER, T. E., NOSRATINIA, A. Diversity through coded cooperation. IEEE Transactions on Wireless Communications, 2006, vol. 5, no. 2, p. 283–289. DOI: 10.1109/TWC.2006.02006
  7. ZHANG, S. W., YANG, F. F., TANG, L., et al. Joint design of QC-LDPC codes for coded cooperation system with joint iterative decoding. International Journal of Electronics, 2015, vol. 103, no. 3, p. 384–405. DOI: 10.1080/00207217.2015.1036374
  8. MUGHAL, S., YANG, F. F., EJAZ, S., et al. Asymmetric turbo code for coded-cooperative wireless communication based on matched interleaver with channel estimation and multi-receive antennas at the destination. RadioEngineering, 2017, vol. 26, no. 3, p. 878–889. DOI: 10.13164/re.2017.0878
  9. MUGHAL, S., YANG, F. F., UMAR, R. Reed-Muller network coded-cooperation with joint decoding. IEEE Communications Letters, 2019, vol. 23, no. 1, p. 24–27. DOI: 10.1109/LCOMM.2018.2879101
  10. UMAR, R., YANG, F., MUGHAL, S., et al. Distributed polarcoded OFDM based on Plotkin's construction for half duplex wireless communication. International Journal of Electronics, 2018, vol. 105, no. 7, p. 1097–1116. DOI: 10.1080/00207217.2018.1426118
  11. MESLEH, R. Y., HAAS, H., SINANOVIC, S., et al. Spatial modulation. IEEE Transactions on Vehicular Technology, 2008, vol. 57, no. 4, p. 2228–2241. DOI: 10.1109/TVT.2008.912136
  12. BASAR, E., AYGOLU, U., PANAYIRCI, E., et al. New trellis code design for spatial modulation. IEEE Transactions on Wireless Communications, 2011, vol. 10, no. 8, p. 2670–2680. DOI: 10.1109/TWC.2011.061511.101745
  13. ZHAO, C., YANG, F., UMAR, R., et al. Two-source asymmetric turbo-coded cooperative spatial modulation scheme with code matched interleaver. Electronics, 2020, vol. 9, no. 1, p. 1–20. DOI: 10.3390/electronics9010169
  14. ZHAO, C., YANG, F., WAWERU, D. K. Reed-Solomon coded cooperative spatial modulation based on nested construction for wireless communication. Radioengineering, 2021, vol. 30, no. 1, p. 172–183. DOI: 10.13164/re.2021.0172
  15. MUGHAL, S., YANG, F., XU, H., et al. Coded cooperative spatial modulation based on multi-level construction of polar code. Telecommunication Systems, 2019, vol. 70, p. 435–446. DOI: 10.1007/s11235-018-0485-6
  16. CHEN, T., VAKILINIA, K., DIVSALAR, D., et al. Protographbased Raptor-like LDPC codes. IEEE Transactions on Communications, 2015, vol. 63, no. 5, p. 1522–1532. DOI: 10.1109/TCOMM.2015.2404842
  17. SUGIURA, S., HANZO, L. Effects of channel estimation on spatial modulation. IEEE Signal Processing Letters, 2012, vol. 19, no. 12, p. 805–808. DOI: 10.1109/LSP.2012.2221707
  18. SIMON, M. K., ALOUINI, M. S. Digital Communication over Fading Channels. 2nd ed. New Jersey (USA): Wiley, 2005. ISBN: 0-471-64953-8
  19. SCHWARTZ, M., BENNETT, W. R., STEIN, S. Communications Systems and Techniques. New York: McGraw-Hill, 1966. ISBN: 0-7803-4715-3
  20. FENG, W., YUAN, J., VUCETIC, B. S. A code-matched interleaver design for turbo codes. IEEE Transactions on Communications, 2002, vol. 50, no. 6, p. 926–937. DOI:10.1109/TCOMM.2002.1010612

Keywords: Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) codes, coded cooperation, Spatial Modulation (SM), half-duplex

K. Bhardwaj, M. Srivastava [references] [full-text] [DOI: 10.13164/re.2022.0374] [Download Citations]
On the Investigation of Frequency-Related Fingerprints of Meminductor/Capacitor and Their Duals Realized by Circuit Emulators

This article investigates the frequency-related fingerprints of the meminductor/capacitors and their duals realized by the circuit emulators. The direct dependency of the hysteresis loop area on the inverse of operating frequency is an important property of the memristor confirming its resistive memory nature. This works shows that not all such elements (which exhibit hysteresis characteristics) seem to follow this fingerprint on subjected to the sinusoidal current/voltage excitation signal when they are realized by the emulator circuits. It is found that in some cases PHL (Pinched Hysteresis Loop) characteristics of the memcapacitor/inductor and their elements, may seem to create a fallacy in their appearance. Although this behaviour is natural (but distinct from the memristor), it does produce some challenges during the measurements of these memelements and non-memelements. The behaviour has been demonstrated in the MATLAB generated plots and also verified in the experimental and simulation results obtained for the designed emulators for the memcapacitor/meminductor and their duals. The paper also attempts to propose potential solutions to avoid this delusion perceived in the PHL characteristics of memcapacitor/meminductor and their duals, due to conventional measuring methods.

  1. CHUA, L. O. Memristor-the missing circuit element. IEEE Transactions on Circuit Theory, 1971, vol. 18, no. 5, p. 507–519. DOI: 10.1109/TCT.1971.1083337
  2. STRUKOV, D. B., SNIDER, G. S., STEWART, D. R., et al. The missing memristor found. Nature, 2008, vol. 453, p. 80–83. DOI: 10.1038/nature06932
  3. YANG, C., CHOI, H., PARK, S., et al. A memristor emulator as a replacement of a real memristor. Semiconductor Science and Technology, 2015, vol. 30, p. 1–9. DOI: 10.1088/0268-1242/30/1/015007
  4. CHUA, L. O. Everything you wish to know about memristors but are afraid to ask. Radioengineering, 2015, vol. 24, no. 2, p. 319–368. DOI: 10.13164/re.2015.0319
  5. BIOLEK, D., BIOLKOVA, V., KOLKA, Z. Memristor pinched hysteresis loops: Touching points. Part I. In International Conference on Applied Electronics. Pilsen (Czechia), 2014, p. 37–40. DOI: 10.1109/AE.2014.7011663
  6. MAIZOUB, S., ELKAWIL, A. S., PSYCHALINOS, C., et al. On the mechanism of creating pinched hysteresis loop using a commercial memristor device. International Journal of Electronics and Communication (AEU), 2019, vol. 111, p. 1–4. DOI: 10.1016/j.aeue.2019.152923
  7. BIOLEK, D., BIOLEK, Z., BIOLKOVA, V., et al. About v-i pinched hysteresis of some non-memristive systems. Mathematical Problems in Engineering, 2018, p. 1–10. DOI: 10.1155/2018/1747865
  8. MOUTTET, B. Memresistors and non-memristive zero crossing hysteresis curves. arXiv:1201.2626v3, 2012, p. 1–8.
  9. ELWAKIL, A. S., FOUDA, M. E., RADWAN, A. G. A simple model of double-loop hysteresis behavior in memristive elements. IEEE Transactions on Circuits and Systems II: Express Briefs, 2013, vol. 60, no. 8, p. 487–491. DOI: 10.1109/TCSII.2013.2268376
  10. ADHIKARI, S. P., SAH, M. P., KIM, H., et al. Three fingerprints of memristor. IEEE Transactions on Circuits and Systems I: Regular Papers, 2013, vol. 60, no. 11, p. 3008–3021. DOI: 10.1109/TCSI.2013.2256171
  11. CHUA, L. O. Resistance switching memories are memristors. Applied Physics A, 2011, vol. 102, p. 765–783. DOI: 10.1007/s00339-011-6264-9
  12. BIOLEK, D., BIOLEK, Z., BIOLKOVA, V., et al. Computing areas of pinched hysteresis loops of mem-systems in OrCAD PSPICE. Applied Mechanics and Materials, 2013, vol. 278–280, p. 1081–1090. DOI: 10.4028/
  13. DI VENTRA, M., PERSHIN, Y. V., CHUA, L. O. Circuit elements with memory: Memristors, memcapacitors, and meminductors. Proceedings of the IEEE, 2009, vol. 97, no. 10, p. 1717–1724. DOI: 10.1109/JPROC.2009.2021077
  14. YIN, Z., TIAN, H., CHEN, G., et al. What are memristor, memcapacitor, and meminductor? IEEE Transactions on Circuits and Systems II: Express Briefs, 2015, vol. 62, no. 4, p. 402–406. DOI: 10.1109/TCSII.2014.2387653
  15. ZHAO, Q., WANG, C., ZHANG, C. X. A universal emulator for memristor, memcapacitor, and meminductor and its chaotic circuit. Chaos, 2019, vol. 56, p. 1–14. DOI: 10.1063/1.5081076
  16. YU, D., ZHAO, X., SUN, T., et al. A simple floating mutator for emulating memristor, memcapacitor, and meminductor. IEEE Transactions on Circuits and Systems II: Express Briefs, 2020, vol. 67, no. 7, p. 1334–1338. DOI: 10.1109/TCSII.2019 2936453
  17. LIU, Y., IU, H. H. Novel floating and grounded memory interface circuits for constructing mem-elements and their applications. IEEE Access, 2020, vol. 8, p. 114761–114772. DOI: 10.1109/ACCESS.2020.3004160
  18. RAJ, N., RANJAN, R. K., KHATEB, F., et al. Mem-elements emulator design with experimental validation and its application. IEEE Access, 2021, vol. 9, p. 69860–69875. DOI: 10.1109/ACCESS.2021.3078189
  19. BIOLEK, D., BIOLKOVA, V., KOLKA, Z., et al. Analog emulator of genuinely floating memcapacitor with piecewiselinear constitutive relation. Circuits, Systems, and Signal Processing, 2016, vol. 35, p. 43–62. DOI: 10.1007/s00034-015-0067-8
  20. ROMERO, F. J., OHATA, A., TORAL-LOPEZ, A., et al. Memcapacitor and meminductor circuit emulators: A review. Electronics, 2021, vol. 10, no. 11, p. 1–21. DOI: 10.3390/electronics10111225
  21. SAH, M, P., BUDHATHOKI, R. K., YANG, C., et al. Charge controlled meminductor emulator. Journal of Semiconductor Technology and Science, 2014, vol. 14, no. 6, p. 750–754. DOI: 10.5573/JSTS.2014.14.6.750
  22. ITOH, M., CHUA, L. Duality of memristor circuits. International Journal of Bifurcation and Chaos, 2013, vol. 23, no. 1. DOI: 10.1142/S0218127413300012
  23. TEXAS INSTRUMENTS. LM13700 Dual Operational Transconductance Amplifiers with Linearizing Diodes and Buffer. Datasheet. 2015. Available at:
  24. SEDRA, A., SMITH, K. C. A second-generation current conveyor and its application. IEEE Transactions on Circuit Theory, 1970, vol. 17, p. 132–134. DOI: 10.1109/TCT.1970.1083067
  25. KAEWDANG, K., SURAKAMPONTORN, W. A wide tunable range CMOS OTA. In 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. Krabi (Thailand), 2008, p. 705–708. DOI: 10.1109/ECTICON.2008.4600528

Keywords: Memelement, memcapacitor, meminductor, PHL

S. Pejoski, Z. Hadzi-Velkov, T. Shuminoski [references] [full-text] [DOI: 10.13164/re.2022.0382] [Download Citations]
Lyapunov Drift-Plus-Penalty Based Resource Allocation in IRS-Assisted Wireless Networks with RF Energy Harvesting

We propose a resource allocation policy for intelligent reflective surface (IRS)-assisted wireless powered communication network (WPCN) where the energy harvesting (EH) users (EHUs) have finite energy storage and data buffers, for storing the harvested energy and the input (sensory) data, respectively. The IRS reflecting coefficients for uplink and downlink are chosen to focus the beam towards a specific EHU, but have additional constant phase offsets (different for uplink and downlink) in order to account for the direct link between the base station and the IRS targeted EHU, and the influence to the EH process of other EHUs in downlink. The EHUs acquire data from their sensors, receive energy in downlink and send information in uplink. We maximize the overall average amount of sensor information in the WPCN by optimizing the IRS reflecting coefficients for the downlink transmissions, the amount of acquired sensor information and the duration of the information transmission period for each EHU in each epoch using the Lyapunov drift-plus-penalty optimization technique. The simulation results demonstrate the effectiveness of the proposed solution.

  1. BASAR, E., RENZO, M. D., ROSNY, J. D., et al. Wireless communications through reconfigurable intelligent surfaces. IEEE Access, 2019, vol. 7, p. 116753–116773. DOI: 10.1109/ACCESS.2019.2935192
  2. WU, Q., ZHANG, R. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Communications Magazine, 2020, vol. 58, no. 1, p. 106–112. DOI: 10.1109/MCOM.001.1900107
  3. BJORNSON, E., SAGUINETTI, L. Power scaling laws and nearfield behaviors of massive MIMO and intelligent reflecting surfaces. IEEE Open Journal of the Communications Society, 2020, vol. 1, p. 1306–1324 . DOI: 10.1109/OJCOMS.2020.3020925
  4. RENCO, M. D., ZAPPONE, A., DEBBAH, M., et al. Smart radio environments empowered by reconfigurable intelligent surfaces: how it works, state of research, and the road ahead. IEEE Journal on Selected Areas in Communications, 2020, vol. 38, no. 11, p. 2450–2525. DOI: 10.1109/JSAC.2020.3007211
  5. KRIKIDIS, I., TIMOTHEOU, S., NIKOLAOU, S., et al. Simultaneous wireless information and power transfer in modern communication systems. IEEE Communications Magazine, 2014, vol. 52, no. 11, p. 104–110. DOI: 10.1109/MCOM.2014.6957150
  6. PEJOSKI, S., HADZI-VELKOV, Z., SCHOBER, R. Optimal power and time allocation for WPCNs with piece-wise linear EH model. IEEE Wireless Communications Letters, 2018, vol. 7, no. 3, p. 364–367. DOI: 10.1109/LWC.2017.2778146
  7. ZHENG, Y., BI, S., ZHANG, Y. J., et al. Intelligent reflecting surface enhanced user cooperation in wireless powered communication networks. IEEE Wireless Communications Letters, 2020, vol. 9, no. 6, p. 901–905. DOI: 10.1109/LWC.2020.2974721
  8. WU, Q., ZHANG, R. Weighted sum power maximization for intelligent reflecting surface aided SWIPT. IEEE Wireless Communications Letters, 2020, vol. 9, no. 5, p. 586–590. DOI: 10.1109/LWC.2019.2961656
  9. LYU, B., HOANG, D. T., GONG, S., et al. Intelligent reflecting surface assisted wireless powered communication networks. In Proceeding of WCNC Workshops 2020. Seoul (Korea), 2020, p. 1–6. DOI: 10.1109/WCNCW48565.2020.9124775
  10. HU, J., ZHANG, H., DI, B., et al. Reconfigurable intelligent surface based RF sensing: Design, optimization, and implementation. IEEE Journal on Selected Areas in Communications, 2020, vol. 38, no. 11, p. 2700–2716. DOI: 10.1109/JSAC.2020.3007041
  11. TANG, W., CHEN, M. Z., CHEN, X., et al. Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement. IEEE Transactions on Wireless Communications, 2021, vol. 20, no. 1, p. 421–439. DOI: 10.1109/TWC.2020.3024887
  12. HADZI-VELKOV, Z., PEJOSKI, S., ZLATANOV, N., et al. Designing wireless powered networks assisted by intelligent reflecting surfaces with mechanical tilt. IEEE Communications Letters, 2022, vol. 25, no. 10, p. 3355–3359. DOI: 10.1109/LCOMM.2021.3098128
  13. NEELY, M. J. Stochastic Network Optimization with Application to Communication and Queuing Systems. Williston (USA): Morgan and Claypool, USA, 2010. ISBN:978-1-60845-455-6
  14. HUANG, L. NEELY, M. J. Utility optimal scheduling in energy harvesting networks. IEEE/ACM Transactions on Networking, 2013, vol. 21, no. 4, p. 1117–1130. DOI: 10.1109/TNET.2012.2230336
  15. SHUMINOSKI, T., JANEVSKI, T. Lyapunov optimization framework for 5G mobile nodes with multi-homing. IEEE Communications Letters, 2016, vol. 20, no. 5, p. 1026–1029. DOI: 10.1109/LCOMM.2016.2540622
  16. GEORGIADIS, L., NEELY, M. J., TASSIULAS, L. Resource allocation and cross-layer control in wireless networks. Foundations and Trends in Networking, 2006, vol. 1, no. 1, p. 1–144. DOI: 10.1561/1300000001
  17. NEELY, M. J. Energy optimal control for time-varying wireless networks. IEEE Transactions on Information Theory, 2006, vol. 52, no. 7, p. 2915–2934. DOI: 10.1109/TIT.2006.876219
  18. SARIKAYA, Y., ERCETIN, O. Self-sufficient receiver with wireless energy transfer in a multi-access network. IEEE Wireless Communications Letters, 2017, vol. 6, no. 4, p. 442–445. DOI: 10.1109/LWC.2017.2701818
  19. GUO, L., CHEN, Z., ZHANG, D., et al. Sustainability in body sensor networks with transmission scheduling and energy harvesting. IEEE Internet of Things Journal, 2019, vol. 6, no. 6, p. 9633–9644. DOI: 10.1109/JIOT.2019.2930076
  20. LAN, X., CHEN, Q., CAI, L., et al. Buffer-aided adaptive wireless powered communication network with finite energy storage and data buffer. IEEE Transactions on Wireless Communications, 2019, vol. 18, no. 12, p. 5764–5779. DOI: 10.1109/TWC.2019.2938958
  21. PATEL, M., WANG, J. Applications, challenges, and prospective in emerging body area networking technologies. IEEE Wireless Communications, 2010, vol. 17, no. 1, p. 80–88. DOI: 10.1109/MWC.2010.5416354

Keywords: Intelligent reflecting surfaces, Lyapunov drift-plus-penalty optimization, wireless powered networks

V. Karsky, M. Tuma [references] [full-text] [DOI: 10.13164/re.2022.0390] [Download Citations]
Design PID Controllers Using Generalized Laguerre Functions

This paper deals with a method of designing PID controllers. Generalized Laguerre functions were used for this task. Generalized Laguerre functions generate an orthogonal base in the time domain and the operator domain. This property of generalized Laguerre functions is beneficially used for the design of the PID controller. Parameters for generalized Laguerre function PID controllers are computed from the Laguerre series of the open loop and the Laguerre series of the ideal open loop. To satisfy this goal, the plant transfer function, the controller transfer function, and the ideal open loop transfer function are transformed into a generalized Laguerre functions base. Three examples are shown to present this method.

  1. ANNASWAMY, A. M., FRADKOV, A. L. A historical perspective of adaptive control and learning. Annual Reviews in Control, 2021, vol. 52, p. 18–41. DOI: 10.1016/j.arcontrol.2021.10.014
  2. ASTROM, K. J., HAGGLUND T. PID Controllers: Theory, Design, and Tuning. Research Triangle Park (North Carolina, USA): ISA - The Instrumentation, Systems and Automation Society, 1995. ISBN: 1556175167
  3. ZAND, J. P., SABOURI, J., KATEBI J., et al. A new time-domain robust anti-windup PID control scheme for vibration suppression of building structure. Engineering Structures, 2021, vol. 244, p. 1–16. DOI: 10.1016/j.engstruct.2021.112819
  4. GUO, L., ZHANG, J. PID control of nonlinear stochastic systems with structural uncertainties. IFAC-PapersOnLine, 2020, vol. 53, no. 2, p. 2189–2194. DOI: 10.1016/j.ifacol.2020.12.002
  5. MA, D., CHEN, J., LIU, A., et al. Explicit bounds for guaranteed stabilization by PID control of second-order unstable delay systems. Automatica, 2019, vol. 100, p. 407–411. DOI: 10.1016/j.automatica.2018.11.053
  6. YU, H., GUAN, Z., CHEN, T., et al. Design of data-driven PID controllers with adaptive updating rules. Automatica, 2020, vol. 121, p. 1–10. DOI: 10.1016/j.automatica.2020.109185
  7. LEE, Y. W. Synthesis of electric networks by means of the Fourier transforms of Laguerre’s functions. Journal of Mathematics and Physics, 1932, vol. 11, no. 1–4, p. 83–113. DOI: 10.1002/sapm193211183
  8. LEE, Y.W. Statistical Theory of Communication. JohnWiley & Sons Inc., 1960. ISBN: 9780471522065
  9. AKCAY, H., NINNESS, B. Orthonormal basis functions for modelling continuous-time systems. Signal Processing, 1999, vol. 77, no. 3, p. 261–274. DOI: 10.1016/S0165-1684(99)00039-0
  10. SAMUEL, E. R., FERRANTI, F., KNOCKAERT, L., et al. Reduced order delayed systems by means of Laguerre functions and Krylov subspaces. In IEEE 18th Workshop on Signal and Power Integrity (SPI). Ghent (Belgium), 2014, p. 1–4. DOI: 10.1109/SaPIW.2014.6844545
  11. HIRAMA, Y., HAMANE, H., HIROKI, F. Closed loop modelling method for non-linear system using Laguerre polynomials. In International Conference on Control, Automation and Systems (ICCAS). Gyeonggi-do (South Korea), 2010, p. 231–236. DOI: 10.1109/ICCAS.2010.5670327
  12. MALTI, R., EKONGOLO, S. B., RAGOT, J. Dynamic SISO and MISO system approximations based on optimal Laguerre models. IEEE Transactions on Automatic Control, 1998, vol. 43, no. 9, p. 1318–1323. DOI: 10.1109/9.718626
  13. DATTOLI, G., GERMANO, B., RICCI, P. E. Laguerre orthogonal functions from an operational point of view. Integral Transforms and Special Functions, 2006, vol. 17, no. 11, p. 779–783. DOI: 10.1080/10652460600856385
  14. TABATABAEI, M. PID controller design based on Laguerre orthonormal functions. Journal of Control Engineering and Applied Informatics, 2016, vol. 18, no. 4, p. 65–76. DOI: 10.1007/s40435-016-0248-8
  15. OLIVIER, P. D. PID controller design using Laguerre series. In Proceedings of the 17th Mediterranean Conference on Control and Automation (MED). Thessaloniki (Greece), 2009. p. 846-851. DOI: 10.1109/MED.2009.5164650
  16. WOLFRAM MATH WORLD. Associated Laguerre Polynomial. 2017, [Online] Cited 2021-12-19. Available at:
  17. WOLFRAM RESEARCH. The Wolfram Functions Site: Polynomials. 2017, [Online] Cited 2021-12-19. Available at:
  18. NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY. NIST Digital Library of Mathematical Functions: Orthogonal Polynomials. 2016, [Online] Cited 2021-12-19. Available at:
  19. BANERJEE, P. On Galois groups of generalized Laguerre polynomials whose discriminants are squares. Journal of Number Theory, 2014, vol. 141, p. 36–58. DOI: 10.1016/j.jnt.2014.01.009
  20. YADAV M. K. Solutions of a system of forced burgers equation in terms of generalized Laguerre polynomials. Acta Mathematica Scientia, 2014, vol. 34B, no. 5, p. 1461–1472. DOI: 10.1016/S0252-9602(14)60096-5
  21. TUMA, M., JURA, P. Dynamical system identification with the generalized Laguerre functions. In 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). Brno (Czech Republic), 2015, p. 220–225. DOI: 10.1109/ICUMT.2015.7382431
  22. TUMA, M., JURA, P. Comparison of different approaches to continuous-time system identification from sampled data. In European Conference on Electrical Engineering and Computer Science (EECS). Bern (Switzerland), 2017, p. 61–65. DOI: 10.1109/EECS.2017.21
  23. TUMA, M., JURA, P. Dead time estimation in generalized Laguerre functions basis. AIP Conference Proceedings, 2020, vol. 2293, no. 1, p. 1–4. DOI: 10.1063/5.0026749
  24. BJORKLUNDS. Experimental Open-Loop Evaluation of Some Time-Delay Estimation Methods in the Laguerre Domain. Technical Report, Linkoping University, 2003.
  25. KARSKY, V. An improved method for parameterizing generalized Laguerre functions to compute the inverse Laplace transform of fractional order transfer functions. AIP Conference Proceedings, 2020, vol. 2293, no. 1, p. 1–4. DOI: 10.1063/5.0026713
  26. KARSKY, V., TUMA, M., JURA, P. Approximation of the fractional order transfer functions with integer order transfer functions. AIP Conference Proceedings, 2022, vol. 2425, no. 1, p. 1–5. DOI: 10.1063/5.0082206
  27. KARSKY, V. Parameterizing generalized Laguerre functions to compute the inverse Laplace transform of fractional order transfer functions. MENDEL - Soft Computing Journal, 2018, vol. 24, no. 1, p. 79–84. DOI:10.13164/mendel.2018.1.079
  28. WOLFRAM MATHWORLD. Gamma Function. 2022, [Online] Cited 2022-2-17. Available at:
  29. BELT, H. J. W., BRINKER, A. C. Optimal parametrization of truncated generalized Laguerre series. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Munich (Germany), 1997, p. 3805–3808. DOI: 10.1109/ICASSP.1997.604708
  30. YUCE, A., DENIZ, F. N., TAN, N., et al. Obtaining the time response of control systems with fractional order PID from frequency responses. In 9th International Conference on Electrical and Electronics Engineering (ELECO). Bursa (Turkey), 2015, p. 832–836. DOI: 10.1109/ELECO.2015.7394522
  31. RADWAN, A. G., SOLIMAN, A. M., ELWAKIL, A. S., et al. On the stability of linear system with fractional-order elements. Chaos, Solitons & Fractals, 2009, vol. 40, no. 5, p. 2317–2328. DOI: 10.1016/j.chaos.2007.10.033
  32. KARSKY, V., TUMA, M. Design PID Controllers Using Generalized Laguerre Functions - source codes. 2021, [Online] Cited 2021-12-19. Available at:
  33. VALSA, J., DVORAK, P., FRIEDL, M. Network model of the CPE. Radioengineering, 2011, vol. 20, no. 3, p. 619–626. ISSN: 1805-9600

Keywords: PID controller, PI controller, PD controller, fractional order systems, generalized Laguerre functions, orthogonal functions

N. Jali, P. Muralidhar, S. R. Patri [references] [full-text] [DOI: 10.13164/re.2022.0398] [Download Citations]
Low Latency SC Decoder Architecture for Interleaved Polar Codes

Interleaved polar (I-Polar) codes, a new facet of polar codes to achieve better channel capacity, is designed by placing the interleaver and deinterleaver blocks midway between the stages of the polar codes. Low latency hardware optimization makes their implementation even more suitable for ultra-reliable low latency applications. This study proposes an optimal hardware design for low latency interleaved polar codes by reframing the last stage of the interleaved successive cancellation decoder. A high-speed adder-subtractor is used to reduce the latency further, thus increasing the speed of operation. Interleaving data in the proposed polar codes augment BER performance compared to conventional (n, k) polar codes. The proposed I-Polar codes are synthesized using Synopsys design compiler (SDC) in CMOS 65-nm technology. Results show that the latency is reduced by 50.5% on average compared to the conventional polar codes as high-speed adder and merged processing elements are used. Moreover, the average gate count and power are reduced by 14% and 40.56%, respectively.

  1. ARIKAN, E. Channel polarization: A method for constructing capacity achieving codes for symmetric binary-input memoryless channels. IEEE Transactions on Information Theory, 2009, vol. 55, no. 7, p. 3051–3073. DOI: 10.1109/TIT.2009.2021379
  2. DIZDAR, O., ARIKAN, E. A high-throughput energy-efficient implementation of successive cancellation decoder for polar codes using combinational logic. IEEE Transactions on Circuits and Systems I: Regular Papers, 2016, vol. 63, no. 3, p. 436–447. DOI: 10.1109/TCSI.2016.2525020
  3. FAN, Y., TSUI, C. An efficient partial-sum network architecture for semi-parallel polar codes decoder implementation. IEEE Transactions on Signal Processing, 2014, vol. 62, no. 12, p. 3165–3179. DOI: 10.1109/TSP.2014.2319773
  4. LEROUX, C., RAYMOND, A. J., SARKIS, G., et al. A semiparallel successive-cancellation decoder for polar codes. IEEE Transactions on Signal Processing, 2013, vol. 61, no. 2, p. 289–299. DOI: 10.1109/TSP.2012.2223693
  5. CHEOLHO, K., HARAM, Y., SABOOH, A., et al. Highthroughput low-complexity successive-cancellation polar decoder architecture using one’s complement scheme. Journal of Semiconductor Technology and Science, 2015, vol. 15, no. 3, p. 427–435. DOI: 10.5573/JSTS.2015.15.3.427
  6. SHRESTHA, R., BANSAL, P., SRINIVASAN, S. High-throughput and high-speed polar-decoder VLSI-architecture for 5G new radio. In 32nd International Conference on VLSI Design and 18th International Conference on Embedded Systems (VLSID). Delhi (India), 2019, p. 329–334. DOI: 10.1109/VLSID.2019.00075
  7. YOON, H., KIM, T. Efficient successive-cancellation polar decoder based on redundant LLR representation. IEEE Transactions on Circuits and Systems II: Express Briefs, 2018, vol. 65, no. 12, p. 1944–1948. DOI: 10.1109/TCSII.2018.2811378
  8. SHRESTHA, R., SAHOO, A. High-speed and hardware-efficient successive cancellation polar-decoder. IEEE Transactions on Circuits and Systems II: Express Briefs, 2019, vol. 66, no. 7, p. 1144–1148. DOI: 10.1109/TCSII.2018.2877140
  9. OH, S., LEE, H. High-performance parallel concatenated polar-CRC decoder Architecture. Journal of Semiconductor Technology and Science, 2018, vol. 18, no. 5, p. 560–567. DOI: 10.5573/JSTS.2018.18.5.560
  10. FAN, Y., XIA, C., CHEN, J., et al. A low-latency list successivecancellation decoding implementation for polar codes. IEEE Journal on Selected Areas in Communications, 2015, vol. 34, no. 2, p. 303–317. DOI: 10.1109/JSAC.2015.2504318
  11. YUAN, B., PARHIL, K. K. Low-latency successive-cancellation list decoders for polar codes with multibit decision. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2015, vol. 23, no. 10, p. 2268–2280. DOI: 10.1109/TVLSI.2014.2359793
  12. TAO, Y., CHO, S. G., ZHANG, Z. A configurable successivecancellation list polar decoder using split-tree architecture. IEEE Journal of Solid-State Circuits, 2021, vol. 56, no. 2, p. 612–623. DOI: 10.1109/JSSC.2020.3005763
  13. SONG, W., ZHOU, H., NIU, K., et al. Efficient successive cancellation stack decoder for polar codes. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2019, vol. 27, no. 11, p. 2608–2619. DOI: 10.1109/TVLSI.2019.2925029
  14. ABBAS, S. M., FAN, Y., CHEN, J., et al. High-throughput and energy-efficient belief propagation polar code decoder. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, 2017, vol. 25, no. 3, p. 1098–1111. DOI: 10.1109/TVLSI.2016.2620998
  15. CHIU, M. Interleaved polar (I-Polar) codes. IEEE Transactions on Information Theory, 2020, vol. 66, no. 4, p. 2430–2442. DOI: 10.1109/TIT.2020.2969155
  16. ROY, J. S., LAKSHMINARAYANAN, G., KO, S. B. High speed architecture for successive cancellation decoder with split-g node block. IEEE Embedded Systems Letters, 2021, vol. 13, no. 3, p. 118–121. DOI: 10.1109/LES.2020.3021144
  17. MISHRA, A., RAYMOND, A. J., AMARU, L. G., et al. A successive cancellation decoder ASIC for a 1024-bit polar code in 180nm CMOS. In IEEE Asian Solid State Circuits Conference (A-SSCC). Kobe (Japan), 2012, p. 205–208. DOI: 10.1109/IPEC.2012.6522661
  18. YUN, H. R., LEE, H. Simplified merged processing element for the successive cancellation polar decoder. Electronics Letters, 2016, vol. 52, no. 4, p. 270–272. DOI: 10.1049/EL.2015.3432

Keywords: BER, deinterleaver, interleaver, I-Polar, latency, ultra-reliable low latency applications

M. T. Mushtaq, S. M. A. Shah, S. Munir, M. Hussain, J. Iqbal, U. H. Khan [references] [full-text] [DOI: 10.13164/re.2022.0406] [Download Citations]
Dual Band Microstrip Semicircular Slot Patch Antenna for WLAN and WIMAX Applications

A dual band microstrip antenna for WIMAX and WLAN applications is analyzed and presented in this paper. The proposed antenna has semicircular slot in patch and Defected Ground Surface (DGS) technique for the improvement of its bandwidth and gain. Computer Simulation Technology (CST) software is used to design and simulate the performance characteristics. The proposed antenna has the dimensions as 28 x 26.6 mm2. The fabricated antenna provides a good reflection coefficient of -48dB and -44.5dB at a center frequency of 3.4GHz and 5.5GHz. Gain achieved by the antenna is 2.72dB and 3.87dB for WLAN and WIMAX application. Good agreements have been found between simulated and measured results. These results confirm that the fabricated antenna is very promising for WLAN and WIMAX applications.

  1. AHMED,H., ZAMAN,W., BASHIR,S., et al. Compact triband slotted printed monopole antenna for WLAN and WiMAX applications. International Journal of RF and Microwave Computer Aided Engineering, 2019, vol. 30, no. 1, p. 1–8. DOI: 10.1002/mmce.21986
  2. HASSAN, M., ARSHAD, F., NAQVI, S. I., et al. A compact flexible and frequency reconfigurable antenna for quintuple applications. Radioengineering, 2017, vol. 26, no. 3, p. 655–661. DOI: 10.13164/re.2017.0655
  3. SOLIMAN, M. S., AL-DWAIRI, M. O., HENDI, A. Y., et al. A compact ultra-wideband patch antenna with dual band-notch performance for WiMAX / WLAN Services. In IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). Amman (Jordan), 2019, p. 831–834. DOI: 10.1109/JEEIT.2019.8717444
  4. HAKIM, M. A., PATWARY, A. B., HOSSAIN, M. A. Design of double inverted F-shaped dual wideband microstrip antenna for WLAN, WiMAX wireless communication. In 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). Dhaka (Bangladesh), 2021, p. 710–714. DOI: 10.1109/ICREST51555.2021.9331058
  5. ALI, W., HAMAD, E., BASSIUNY, M., et al. Complementary split ring resonator based triple band microstrip antenna for WLAN/WiMAX applications. Radioengineering, 2017, vol. 26, no. 1, p. 78–84. DOI: 10.13164/re.2017.0078
  6. RANI, S. S., DATTATREYA, G., PALLA, R. K., et al. Design of inverted U-shaped radiating patch antenna for LTE/WiMAX applications. In IEEE Indian Conference on Antennas and Propogation (InCAP). Hyderabad (India), 2018, p. 1–4. DOI: 10.1109/INCAP.2018.8770952
  7. ASOKAN, V., THILAGAM, S., KUMAR, V. K. Design and analysis of microstrip patch antenna for 2.4 GHz ISM band and WLAN application. In IEEE International Conference on Electronics and Communication System (ICECS). Coimbatore (India), 2015, p. 1114–1118. DOI: 10.1109/ECS.2015.7124756
  8. SIPAL, D., ABEGAONKAR, M. P., KOUL, S. K. Compact planar 3.5/5.5 GHz dual band MIMO USB dongle antenna for WiMAX applications. In IEEE Indian Conference on Antennas and Propagation (InCAP). Hyderabad (India), 2018, p. 1–4. DOI: 10.1109/INCAP.2018.8770899
  9. SAEED, S. M., BALANIS, C. A., BIRTCHER, C. R. Inkjet-printed flexible reconfigurable antenna for conformal WLAN/WiMAX wireless devices. IEEE Antennas andWireless Propagation Letters, 2016, vol. 15, p. 1979–1982. DOI: 10.1109/LAWP.2016.2547338
  10. TIWARI, P. K., VERMA, S. Circulary polarized wide-slot printed antenna for WiMAX and WLAN applications. In Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology (ICECA). Coimbatore (India), 2018, p. 1224–1229. DOI: 10.1109/ICECA.2018.8474719
  11. ARSHAD, S., AHMED, A., SHEIKH, Z., et al. A compact dualband circularly polarized asymmetric patch antenna for WLAN applications. In Proceedings of Asia Pacific Microwave Conference (APMC). Kuala Lumpur (Malaysia), 2017, p. 952–955. DOI: 10.1109/APMC.2017.8251608
  12. SHI, F., JIANG, T., LI, Y. A fork-like dual-band antenna with an inverted U-shaped parasitic element for WLAN and WIMAX applications. In Progress In Electromagnetics Research Symposium - Fall (PIERS - FALL). Singapore, 2017, p. 124–127. DOI: 10.1109/PIERS-FALL.2017.8293123
  13. LI, S., MAO, Y., ELSHERBENI, A. Z. A novel miniaturized WLAN/WiMAX antenna inspired with metamaterial. In International Applied Computational Electromagnetics Society Symposium (ACES). Nanjing (China), 2019, p. 1–3. DOI: 10.23919/ACES48530.2019.9060615
  14. HABIBA, H. U., BABU, A. S. P., BALASUBRAMANIAN, A. N., et al. Design of a 3.3 GHz monopole antenna for WiMAX portable device. In International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). Chennai (India), 2017, p. 268–270. DOI: 10.1109/WiSPNET.2017.8299760
  15. MUJAHIDIN, I. Characterization of 5.5 GHz high gain microstrip 2x2 array antenna. Journal of Electrical Engineering, Mechatronic and Computer Science, 2020, vol. 3, no. 2, p. 135–142. DOI: 10.26905/jeemecs.v3i2.4332
  16. ANITHA, V. R., CHO, M., SHIM, J. Design of two by three element fractal tree antenna array for WLAN and WiFi applications. Journal of Communications Technology and Electronics, 2017, vol. 62, no. 1, p. 61–65. DOI: 10.1134/S1064226917010016
  17. KUNDU, A., CHAKRABORTY, U., BHATTACHARJEE, A. K. Design of a compact wide band microstrip antenna with very low VSWR for WiMAX applications. International Journal of Microwave and Wireless Technologies, 2016, vol. 9, no. 3, p. 1–6. DOI: 10.1017/S1759078716000374
  18. ANAND, S., ROKHINI, D. A double line SIW cavity backed antenna for WLAN applications. International Journal of RF and Microwave Computer Aided Engineering, 2019, vol. 29, no. 9, p. 1–9. DOI: 10.1002/mmce.21861
  19. KUNWAR, A., GAUTAM, A. K., KANAUJIA, B. K., et al. Circularly polarized D-shaped slot antenna for wireless applications. International Journal of RF and Microwave Computer Aided Engineering, 2018, vol. 29, no. 1, p. 1–10. DOI: 10.1002/mmce.21498
  20. JOSHI, M. P., GOND, V. Dual band circularly polarized square microstrip patch antenna for WLAN and Wi-MAX. In IEEE Applied Electromagnetics Conference (AEMC). Aurangabad (India), 2017, p. 1–2. DOI: 10.1109/AEMC.2017.8325736
  21. KUMAR, R., CHAUDHARY, R. K. A new bidirectional wideband circularly polarized cylindrical dielectric resonator antenna using modified J-shaped ground plane for WiMAX/LTE applications. Radioengineering, 2019, vol. 28, no. 2, p. 391–398. DOI: 10.13164/re.2019.0391
  22. KHAN,U. H., ASLAM, B., AZAM, M., et al. Compact RFID enabled moisture sensor. Radioengineering, 2016, vol. 25, no. 3, p. 449–456. DOI: 10.13164/re.2016.0449

Keywords: WLAN, WIMAX, dual-band, DGS, CST

D. Krolak, P. Horsky [references] [full-text] [DOI: 10.13164/re.2022.0413] [Download Citations]
An EMC Susceptibility Study of Integrated Basic Bandgap Voltage Reference Cores

This paper presents a comparative EMC susceptibility study of various integrated bandgap voltage reference cores. Conventional well-known bandgap references based on Kuijk, Brokaw and Tsividis concepts with reduced count of bipolar junction transistors in the core were analyzed. On top of the EMC susceptibility comparison, basic parameters like temperature drift, sensitivity to an operational amplifier input offset and line regulation are also discussed. The influence of a collector leakage current compensation at high temperatures is investigated as well.

  1. KOK, C. W., TAM, W. S. CMOS Voltage References: An Analytical and Practical Perspective. Singapore: John Wiley and Sons, 2013. DOI: 10.1002/9781118275696
  2. HUIJSING, J., VAN DE PLASSCHE, R. J., SANSEN, W. M. C. (Eds.) Analog Circuit Design, Low-Noise, Low-Power, Low-Voltage; Mixed-Mode Design with CAD Tools; Voltage, Current and Time References. Springer, 1996, VIII, 422 p. DOI: 10.1007/978-1-4757-2462-2
  3. DUAN, Q., ROH, J. A 1.2-V 4.2-ppm/°C high-order curvaturecompensated CMOS bandgap reference. IEEE Transactions on Circuits and Systems, 2015, vol. 62, no. 3, p. 662–670. DOI: 10.1109/TCSI.2014.2374832
  4. KANDADAI, H. Comparison of Kuijk and Brokaw voltage reference architectures for high precision on-chip references. International Journal of Industrial Electronics and Electrical Engineering (IJIEEE), 2016, vol. 4, no. 8, p. 71–74. ISSN: 2347-6982. Available at:
  5. SINGH, K. J., MEHRA, R., HANDE, V. Ultra low power, trimless and resistor-less bandgap voltage reference. In Proceedings of 13th International Conference on Industrial and Information Systems (ICIIS). Rupnagar (India), 2018, p. 292–296. DOI: 10.1109/ICIINFS.2018.8721310
  6. HUANG, W., LIU, L., ZHU, Z. A sub-200nW all-in-one bandgap voltage and current reference without amplifiers. IEEE Transactions on Circuits and Systems-II: Express Briefs, 2021, vol. 68, no. 1, p. 121–125. DOI: 10.1109/TCSII.2020.3007195
  7. HARTL, P. An accurate voltage reference for automotive applications. Electroscope, 2014, no. 3, p. 1–5. ISSN: 1802-4564. Available at:
  8. OSMANOVIĆ, D., SKELEDZIJA, I., SPOLJARIĆ, K., et al. Design of a tunable temperature coefficient voltage reference with low-dropout voltage regulator in 180-nm CMOS technology. In Proceedings of 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). Opatija (Croatia), 2020, p. 93–98. DOI: 10.23919/MIPRO48935.2020.9245163
  9. KROLAK, D., PLOJHAR, J., HORSKY, P. An automotive lowpower EMC robust Brokaw bandgap voltage reference. IEEE Transactions on Electromagnetic Compatibility, 2020, vol. 62, no. 5, p. 2277–2284. DOI: 10.1109/TEMC.2019.2958926
  10. RAZAVI, B. The bandgap reference [A circuit for all seasons]. IEEE Solid-State Circuits Magazine, 2016, vol. 8, no. 3, p. 9–12. DOI: 10.1109/MSSC.2016.2577978
  11. SANSEN, W. Bandgap and current reference circuits. Analog Design Essentials. The International Series in Engineering and Computer Science, 2006, vol. 859. Boston (MA): Springer. DOI: 10.1007/0-387-25747-0_16
  12. RADOIAS, L., ZEGHERU, C., BREZEANU, G. Substrate leakage current influence on bandgap voltage references in automotive applications. In Proceedings of CAS 2012 (International Semiconductor Conference). Sinaia (Romania), 2012, p. 389–392. DOI: 10.1109/SMICND.2012.6400752
  13. YANG, S., MAK, P.-I., MARTINS, R. P. A 104μW EMI-resisting bandgap voltage reference achieving –20dB PSRR, and 5% DC shift under a 4dBm EMI level. In Proceedings of 2014 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). Ishigaki (Japan), 2014, p. 57–60. DOI: 10.1109/APCCAS.2014.7032718
  14. MA, Y., BAI, CH., WANG, Y., et al. A low noise CMOS bandgap voltage reference using chopper stabilization technique. In Proceedings of 5th International Conference on Integrated Circuits and Microsystems. Nanjing (China), 2020, p. 184–187. DOI: 10.1109/ICICM50929.2020.9292198

Keywords: Bandgap voltage reference, Brokaw, BCD, EMC, HF immunity, Kuijk, offset, temperature drift, Tsividis

Z. Ding, J. Zhang, Y. Liu, J. Wang, G. Chen, L. Cao [references] [full-text] [DOI: 10.13164/re.2022.0422] [Download Citations]
Spectrum Map Construction Based on Optimized Sensor Selection and Adaptive Kriging Model

Spectrum map (SM) is an important tool to reflect the spectrum usage in the electromagnetic environment. To address the problems of low precision and poor efficiency in the SM construction, this paper develops a novel SM construction approach based on the artificial bee colony enabled sensor layout optimization and an adaptive Kriging model based on spatial autocorrelation. Considering the significant autocorrelation between sensor attributes caused by the exponentially decaying shadow fading of signal propagation, the sensor estimation groups are established, and the estimation results are obtained by the Kriging model. The simulation results show that the proposed SM construction scheme can not only effectively reduce the overhead of sensor resources but also obtain a high SM construction accuracy. Extensive simulation results show that the proposed method can reduce the RMSE of SM construction by 37.56%, 25.32% and 12.89% respectively compared with Random-OK when the standard deviation of shadow fading is 1 dB, 3 dB and 6 dB. Erratum to Fig. 7 of this paper is available via link:

  1. MADAN, H. T., BASARKOD P. I. A survey on efficient spectrum utilization for future wireless networks using cognitive radio approach. In 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (ICATCCT). Mangalore (India), 2018, p. 47–53. DOI: 10.1109/iCATccT44854.2018.9001951
  2. WANG, B., LIU, K. R. Advances in cognitive radio networks: A survey. IEEE Journal of Selected Topics in Signal Processing, 2011, vol. 5, no. 1, p. 5–23. DOI: 10.1109/JSTSP.2010.2093210
  3. CHEN, Y., YU, G., ZHANG, Z., et al. On cognitive radio networks with opportunistic power control strategies in fading channels. IEEE Transactions on Wireless Communications, 2008, vol. 7, no. 7, p. 2752–2761. DOI: 10.1109/TWC.2008.070145
  4. HATTAB, G., IBNKAHLA, M. Multiband spectrum access: Great promises for future cognitive radio networks. Proceedings of the IEEE, 2014, vol. 102, no. 3, p. 282–306. DOI: 10.1109/JPROC.2014.2303977
  5. KALATHIL, D. M., JAIN, R. Spectrum sharing through contracts for cognitive radios. IEEE Transactions on Mobile Computing, 2013, vol. 12, no. 10, p. 1999–2011. DOI: 10.1109/TMC.2012.171
  6. OGBODO, E. U., DORRELL, D., ABU-MAHFOUZ, A. M. Cognitive radio based sensor network in smart grid: Architectures, applications and communication technologies. IEEE Access, 2017, vol. 5, p. 19084–19098. DOI: 10.1109/ACCESS.2017.2749415
  7. KARABOGA, D., AKAY, B. Artificial Bee Colony (ABC) algorithm on training artificial neural networks. In 2007 IEEE 15th Signal Processing and Communications Applications. Eskisehir (Turkey), 2007, p. 1–4. DOI: 10.1109/SIU.2007.4298679
  8. OLIVER, M. A., WEBSTER, R. A tutorial guide to geostatistics: Computing and modelling variograms and kriging. CATENA, 2014, vol. 113, p. 56–69. DOI: 10.1016/j.catena.2013.09.006
  9. SATO, K., INAGE, K., FUJII, T. On the performance of neural network residual Kriging in radio environment mapping. IEEE Access, 2019, vol. 7, p. 94557–94568. DOI: 10.1109/ACCESS.2019.2928832
  10. XIA, H., ZHA, S., HUANG, J., et al. Radio environment map construction by adaptive ordinary Kriging algorithm based on affinity propagation clustering. International Journal of Distributed Sensor Networks, 2020, vol. 16, no. 5, p. 1–10. DOI: 10.1177/1550147720922484
  11. HAN, Z., LIAO, J., QI, Q., et al. Radio environment map construction by Kriging algorithm based on mobile crowd sensing. Wireless Communications and Mobile Computing, 2019, vol. 2019, p. 1–12. DOI: 10.1155/2019/4064201
  12. OKOBIAH, O., MOHANTY, S. P., KOUGIANOS, E. Ordinary Kriging metamodel-assisted ant colony algorithm for fast analog design optimization. In Thirteenth International Symposium on Quality Electronic Design. Santa Clara (USA), 2012, p. 458–463. DOI: 10.1109/ISQED.2012.6187533
  13. WANG, Z., LING, C. On the geometric ergodicity of Metropolis-Hastings algorithms for lattice Gaussian sampling. IEEE Transactions on Information Theory, 2018, vol. 64, no. 2, p. 738–751. DOI: 10.1109/TIT.2017.2742509
  14. WANG, Z., LING, C. Lattice Gaussian sampling by Markov chain Monte Carlo: Bounded distance decoding and trapdoor sampling. IEEE Transactions on Information Theory, 2019, vol. 65, no. 6, p. 3630–3645. DOI: 10.1109/TIT.2019.2901497
  15. FAINT, S., URETEN, O., WILLINK, T. Impact of the number of sensors on the network cost and accuracy of the radio environment map. In CCECE 2010. Calgary (Canada), 2010, p. 1–5. DOI: 10.1109/CCECE.2010.5575188
  16. SUCHAŃSKI, M., KANIEWSKI, P., ROMANIK, J., et al. Radio environment maps for military cognitive networks: Density of small-scale sensor network vs. map quality. EURASIP Journal on Wireless Communications and Networking, 2020, vol. 2020, no. 1, p. 1–20. DOI: 10.1186/s13638-020-01803-4
  17. TANG, M., DING, G., WU, Q., et al. A joint tensor completion and prediction scheme for multi-dimensional spectrum map construction. IEEE Access, 2016, vol. 4, p. 8044–8052. DOI: 10.1109/ACCESS.2016.2627243
  18. SUN, J., WANG, J., DING, G., et al. Long-term spectrum state prediction: An image inference perspective. IEEE Access, 2018, vol. 6, p. 43489–43498. DOI: 10.1109/ACCESS.2018.2861798
  19. GE, C., WANG, Z., ZHANG, X. Robust long-term spectrum prediction with missing values and sparse anomalies. IEEE Access, 2019, vol. 7, p. 16655–16664. DOI: 10.1109/ACCESS.2018.2889161
  20. KANIEWSKI, P., ROMANIK, J., GOLAN, E., et al. Spectrum awareness for cognitive radios supported by radio environment maps: Zonal approach. Applied Sciences, 2021, vol. 11, no. 7, p. 1–23. DOI: 10.3390/app11072910
  21. CHAUDHARI, S., KOSUNEN, M., MAKINEN, S., et al. Spatial interpolation of cyclostationary test statistics in cognitive radio networks: Methods and field measurements. IEEE Transactions on Vehicular Technology, 2018, vol. 67, no. 2, p. 1113–1129. DOI: 10.1109/TVT.2017.2717379
  22. SATO, K., SUTO, K., INAGE, K., et al. Space-frequency interpolated radio map. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 1, p. 714–725. DOI: 10.1109/TVT.2021.3049894
  23. GUDMUNDSON, M. Correlation model for shadow fading in mobile radio systems. Electronics Letters, 1991, vol. 27, no. 23, p. 2145–2146. DOI: 10.1049/el:19911328
  24. HE, R., ZHONG, Z., AI, B., et al. Shadow fading correlation in high-speed railway environments. IEEE Transactions on Vehicular Technology, 2015, vol. 64, no. 7, p. 2762–2772. DOI: 10.1109/TVT.2014.2351579
  25. KAMMER, D. C. Sensor placement for on-orbit modal identification and correlation of large space structures. Journal of Guidance Control and Dynamics, 1991, vol. 14, no. 2, p. 251–259. DOI: 10.2514/3.20635
  26. CHEN, Y. New approaches for calculating Moran’s index of spatial autocorrelation. PLOS ONE, 2013, vol. 8, no. 7. DOI: 10.1371/journal.pone.0068336
  27. OLEA, R. A. Geostatistics for Engineers and Earth Scientists. US: Springer, 1999. ISBN: 9781461372714
  28. THRANE, J., ZIBAR, D., CHRISTIANSEN, H. L. Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz. IEEE Access, 2020, vol. 8, p. 7925–7936. DOI: 10.1109/ACCESS.2020.2964103
  29. THRANE, J., CHRISTIANSEN, H. L. Mobile communication system measurements and satellite images. IEEE Dataport, 2019. DOI: 10.21227/1xf4-eg98
  30. ROHDE-SCHWARZ. R&S TSMW Universal Radio Network Analyzers User Manual. 2017. [Online] Available at:
  31. SUCHAŃSKI, M., KANIEWSKI, P., ROMANIK, J., et al. Radio environment map to support frequency allocation in military communications systems. In 2018 Baltic URSI Symposium (URSI). Poznan (Poland), 2018, p. 230–233. DOI: 10.23919/URSI.2018.8406717
  32. ROMANIK, J., GOLAN, E., ZUBEL, K., et al. Electromagnetic situational awareness of cognitive radios supported by radio environment maps. In 2019 Signal Processing Symposium (SPSympo). Krakow (Poland), 2019, p. 1–6. DOI: 10.1109/SPS.2019.8882065

Keywords: Spectrum map, sensor layout optimization, adaptive Kriging model, spatial autocorrelation, artificial bee colony

S. Chatterjee, B. Bandyopadhyay, S. Chatterjee, A. Majumdar [references] [full-text] [DOI: 10.13164/re.2022.0431] [Download Citations]
Least Perturbation Based Method of Multi-Objective Null Placement in Linear Antenna Array Using Evolutionary Algorithms

The paper proposes a novel least perturbation based method of constrained null placement for a non-uniformly excited linear antenna array. Synthesis of am¬plitude and phase of edge element using least perturbation based analytical technique for required null placement leads to degradation of pattern in terms of increased side lobe level and beam broadening. Further computation capability of the method of least perturbation has been enhanced using an evolutionary algorithm. Subsequently, suitable evolutionary algorithms have been employed to find the optimum value of excitation and phase of edge elements subject to constraints of side lobe level reduction, beamwidth narrowing, and main beam control. Design of 8 and 15 elements linear array with a 95% reduction in com-putation time elucidates the capabilities of the proposed method. Further 3D electromagnetic solver -based valida¬tion process has been used to ascertain the practical acceptability of the method.

  1. SCHELKUNOFF, S. A. A mathematical theory of linear arrays. Bell System Technical Journal, 1943, vol. 22, no. 1, p. 80–107. DOI: 10.1002/j.1538-7305.1943.tb01306.x
  2. APPLEBAUM, S. Adaptive arrays. IEEE Transactions on Antennas and Propagation, 1976, vol. 24, no. 5, p. 585–598. DOI: 10.1109/TAP.1976.1141417
  3. EL-AZHARY, I., AFIFI, M. S., EXCELL, P. S. A simple algorithm for sidelobe cancellation in a partially adaptive linear array. IEEE Transactions on Antennas and Propagation, 1988, vol. 36, no. 10, p. 1484–1486. DOI: 10.1109/8.8637
  4. IBRAHIM, H. M. Null steering by real weight control- A method of decoupling the weights. IEEE Transactions on Antennas and Propagation, 1991, vol. 39, no. 11, p. 1648–1650. DOI: 10.1109/8.102781
  5. ISMAIL, T. H., DAWOUD, M. M. Null steering in phased arrays by controlling the element positions. IEEE Transactions on Antennas and Propagation, 1991, vol. 39, no. 11, p. 1561–1566. DOI: 10.1109/8.102769
  6. HAUPT, R. L., HAUPT, S. E. Phase only adaptive nulling with a genetic algorithm. In IEEE Aerospace Conference. Snowmass (CO, USA), 1997, p. 151–160. DOI: 10.1109/AERO.1997.574858
  7. HEJRES, J. A. Null steering in phased arrays by controlling the positions of selected elements. IEEE Transactions on Antennas and Propagation, 2004, vol. 52, no. 11, p. 2891–2895. DOI: 10.1109/TAP.2004.835128
  8. DONELLI, M., AZARO, R., DE NATALE, F. G. B., et al. An innovative computational approach based on a particle swarm strategy for adaptive phase arrays control. IEEE Transactions on Antennas and Propagation, 2006, vol. 54, no. 3, p. 888–897. DOI: 10.1109/TAP.2006.869912
  9. MOUHAMADOU, M., ARMAND, P., VAUDON, P., et al. Interference suppression of the linear antenna arrays controlled by phase with use of SQP algorithm. Progress in Electromagnetic Research, 2006, vol. 59, p. 251–265. DOI: 10.2528/PIER05100603
  10. LENG, S., SER, W., KO, C. C. A simple constrained based adaptive null steering algorithm. In Proceedings of the 16th European Signal Processing Conference. Lausanne (Switzerland), 2008, p. 1–5. ISSN: 2219-5491
  11. GUNES, F., TOKAN, F. Pattern search optimization with applications on synthesis of linear antenna arrays. Expert Systems with Applications, 2010, vol. 37, no. 6, p. 4698–4705. DOI: 10.1016/j.eswa.2009.11.012
  12. ZUNIGA, V., ERDOGAN, A. T., ARSLAN, T. Adaptive radiation pattern optimization for antenna arrays by phase perturbations using particle swarm optimization. In Proceedings of NASA/ESA Conference on Adaptive Hardware and Systems. Anaheim (CA, USA), 2010, p. 209–214. DOI: 10.1109/AHS.2010.5546256
  13. LI, X., YIN, M. Optimal synthesis of linear antenna array with composite differential evolution algorithm. Scientia Iranica, 2012, vol. 19, no. 6, p. 1780–1787. DOI: 10.1016/j.scient.2012.03.010
  14. BHATTACHARYA, R., BHATTACHARYA, T. K., GARG, R. Position mutated hierarchical particle swarm optimization and its application in synthesis of unequally spaced antenna arrays. IEEE Transactions on Antennas and Propagation, 2012, vol. 60, no. 7, p. 3174–3181. DOI: 10.1109/TAP.2012.2196917
  15. MEHMOOD, S., KHAN, Z. U., ZAMAN, F., et al. Performance analysis of the different null steering techniques in the field of adaptive beamforming. Research Journal of Applied Sciences, Engineering and Technology, 2013, vol. 5, no. 15, p. 4006–4012. DOI: 10.19026/RJASET.5.4468
  16. RECIOUI, A., BENTARZI, H. Null steering of Dolph-Chebycheff arrays using Taguchi method. The International Arab Journal of Information Technology, 2013, vol. 10, no. 2, p. 120–125.
  17. GOSWAMI, B., MANDAL, D. A genetic algorithm for the level control of nulls and side lobes in linear antenna arrays. Journal of King Saud University-Computer and Information Sciences, 2013, vol. 25, no. 2, p. 117–126. DOI: 10.1016/j.jksuci.2012.06.001
  18. MOHAMMED, J. R., SAYIDMARIE, K. H. Null steering method by controlling two elements. IET Microwaves, Antennas and Propagation, 2014, vol. 15, no. 8, p. 1348–1355. DOI: 10.1049/iet-map.2014.0213
  19. BANERJEE, S., DWIVEDI, V. V. Linear array synthesis using Schelkunoff polynomial method and particle swarm optimization. In 2015 International Conference on Advances in Computer Engineering and Applications. Ghaziabad (India), 2015, p. 1–4. DOI: 10.1109/ICACEA.2015.7164785
  20. AL-AZZA, A. A., AL-JODAH, A. A., HARACKIEWICZ, F. J. Spider monkey optimization: A novel technique for antenna optimization. IEEE Antennas and Wireless Propagation Letters, 2015, vol. 15, p. 1016–1019. DOI: 10.1109/LAWP.2015.2490103
  21. SAXENA, P., KOTHARI, A. Linear antenna array optimization using flower pollination algorithm. SpringerPlus, 2016, vol. 5, p. 1–15. DOI: 10.1186/s40064-016-1961-7
  22. RAHMAN, S. U., CAO, Q., AHMED, M. M., et al. Analysis of linear antenna array for minimum side lobe level, half power beamwidth, and nulls control using PSO. Journal of Microwaves, Optoelectronics and Electromagnetic Applications, 2017, vol. 16, p. 577–591. DOI: 10.1590/2179-10742017v16i2913
  23. SINGH, U., SALGOTRA, R. Synthesis of linear antenna array using flower pollination algorithm. Neural Computing and Applications, 2018, vol. 29, p. 435–445. DOI: 10.1007/s00521-016-2457-7
  24. MOHAMMED, J. R. Obtaining wide steered nulls in linear array patterns by optimizing the locations of two edge elements. AEU International Journal of Electronics and Communications, 2019, vol. 101, p. 145–151. DOI: 10.1016/j.aeue.2019.02.004
  25. SUBHASHINI, K. R. Antenna array synthesis using a newly evolved optimization approach: Strawberry algorithm. Journal of Electrical Engineering, 2019, vol. 70, no. 4, p. 317–322. DOI: 10.2478/jee-2019-0062
  26. HAMZA, A., ATTIA, H. Fast beam steering and null placement in an adaptive circular antenna array. IEEE Antennas and Wireless Propagation Letters, 2020, vol. 19, no. 9, p. 1561–1565. DOI: 10.1109/LAWP.2020.3009905
  27. JAMUNAA, D., MAHANTI, G. K., HASOON, F. N. Synthesis of phase-only position optimized reconfigurable uniformly excited linear antenna arrays with a single null placement. Journal of King Saud University-Engineering Sciences, 2020, vol. 32, no. 6, p. 360–367. DOI: 10.1016/j.jksues.2019.04.005
  28. OWOOLA, E. O., XIA, K., WANG, T., et al. Pattern synthesis of uniform and sparse linear antenna array using mayfly algorithm. IEEE Access, 2021, vol. 9, p. 77954–77975. DOI: 10.1109/ACCESS.2021.3083487
  29. WANG, A., LI, X., XU, Y. BA-based low-PSLL beam pattern synthesis in the presence of array errors. IEEE Access, 2022, vol. 10, p. 9371–9379. DOI: 10.1109/ACCESS.2022.3143577
  30. YANG, C., OU, N., DENG, Y., et al. Pattern synthesis algorithm for range ambiguity suppression in the LT-1 mission via sequential convex optimizations. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–13. DOI: 10.1109/TGRS.2021.3099132
  31. RAY, K. P. Design aspects of printed monopole antennas for ultrawide band applications. International Journal of Antennas and Propagation, 2008, p. 1–8. DOI: 10.1155/2008/713858

Keywords: Null placement, beam steered linear array, minimum perturbation, excitation distribution, evolutionary algorithms

G. Y. Wang, A. Dhaka, T. Teng, K. Yu [references] [full-text] [DOI: 10.13164/re.2022.0440] [Download Citations]
Energy Efficiency Optimization for D2D Underlay Communication in Distributed Antenna System over Composite Fading Channels

Device-to-Device (D2D) communication is a potential technology to improve the spectral and energy efficiency (EE) of communication networks. In this paper, we study energy-efficient power allocation (PA) schemes in uplink distributed antenna system (DAS) with device-to-device underlay communication. Our goal is to maximize the total EE of all D2D pairs while guaranteeing the data rate and transmit power requirements of the cellular user and D2D links. To solve this non-convex constrained optimization problem, we propose an energy-efficient near-optimal PA algorithm based on the concave-convex procedure and fractional programming theory. This near-optimal algorithm can achieve the EE performance close to the optimal exhaustive search. To reduce the complexity, we furthermore present an efficient sub-optimal algorithm with the antenna selection method which can obtain the closed-form power allocation expressions. Simulation results demonstrate the significant EE performance of our proposed PA schemes.

  1. TEHRANI, M. N., UYSAL, M., YANIKOMEROGLU, H. Deviceto-device communication in 5G cellular networks: Challenges, solutions, and future directions. IEEE Communications Magazine, 2014, vol. 52, no. 5, p. 86–92. DOI: 10.1109/MCOM.2014.6815897
  2. GE, X., TU, S., MAO, G., et al. 5G ultra-dense cellular networks. IEEE Wireless Communications, 2016, vol. 23, no. 1, p. 72–79. DOI: 10.1109/MWC.2016.7422408
  3. ZHANG, S., LIU, J., GUO, H., et al. Envisioning device-to-device communications in 6G. IEEE Network, 2020, vol. 34, no. 3, p. 86–91. DOI: 10.1109/MNET.001.1900652
  4. YU, C., DOPPLER, K., RIBEIRO, C. B., et al. Resource sharing optimization for device-to-device communication underlaying cellular networks. IEEE Transactions on Wireless Communications, 2011, vol. 10, no. 8, p. 2752–2763. DOI: 10.1109/TWC.2011.060811.102120
  5. YU, X., CHU, J., YU, K., et al. Energy-efficiency optimization for iot-distributed antenna systems with SWIPT over composite fading channels. IEEE Internet of Things Journal, 2020, vol. 7, no. 1, p. 197–207. DOI: 10.1109/JIOT.2019.2946581
  6. HE, C., SHENG, B., ZHU, P., et al. Energy efficiency and spectral efficiency tradeoff in downlink distributed antenna systems. IEEE Wireless Communications Letters, 2012, vol. 1, no. 3, p. 153–156. DOI: 10.1109/WCL.2012.022812.120048
  7. ZHANG, J., ZHANG, Y., XIANG, L., et al. Robust energyefficient transmission for wireless-powered D2D communication networks. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 8, p. 7951–7965. DOI: 10.1109/TVT.2021.3095626
  8. RAMEZANI-KEBRYA, A., DONG, M., LIANG, B., et al. Joint power optimization for device-to-device communication in cellular networks with interference control. IEEE Transactions on Wireless Communications, 2017, vol. 16, no. 8, p. 5131–5146. DOI: 10.1109/TWC.2017.2706259
  9. FENG, D., LU, L., WU, Y., et al. Device-to-device communications underlaying cellular networks. IEEE Transactions on Communications, 2013, vol. 61, no. 8, p. 3541–3551. DOI: 10.1109/TCOMM.2013.071013.120787
  10. WANG, Q., SHEN, X. Power optimization in device to device communications underlying 5G cellular networks. Radioengineering, 2022, vol. 31, no. 1, p. 94–103. DOI: 10.13164/re.2022.0094
  11. XU, Y., LIU, Z., HUANG, C. Robust resource allocation algorithm for energy-harvesting-based D2D communication underlaying UAV-assisted networks. IEEE Internet of Things Journal, 2021, vol. 8, no. 23, p. 17161–17171. DOI: 10.1109/JIOT.2021.3078264
  12. KAI, C., WU, Y., PENG, M., et al. Joint uplink and downlink resource allocation for NOMA-enabled D2D communications. IEEE Wireless Communications Letters, 2021, vol. 10, no. 6, p. 1247–1251. DOI: 10.1109/LWC.2021.3063169
  13. WU, Y., WANG, J., QIAN, L., et al. Optimal power control for energy efficient D2D communication and its distributed implementation. IEEE Communications Letters, 2015, vol. 19, no. 5, p. 815–818. DOI: 10.1109/LCOMM.2015.2407871
  14. WANG, F., XU, C., SONG, L., et al. Energy-efficient resource allocation for device-to-device underlay communication. IEEE Transactions on Wireless Communications, 2015, vol. 14, no. 4, p. 2082–2092. DOI: 10.1109/TWC.2014.2379653
  15. ZHAO, W., WANG, S. Resource allocation for device-to-device communication underlaying cellular networks: An alternating optimization method. IEEE Communications Letters, 2015, vol. 19, no. 8, p. 1398–1401. DOI: 10.1109/LCOMM.2015.2444403
  16. WU, Q., LI, G., CHEN, W., et al. Energy-efficient D2D overlaying communications with spectrum-power trading. IEEE Transactions on Wireless Communications, 2017, vol. 16, no. 7, p. 4404–4419. DOI: 10.1109/TWC.2017.2698032
  17. HU, J., HENG, W., LI, X., et al. Energy-efficient resource reuse scheme for D2D communications underlaying cellular networks. IEEE Communications Letters, 2017, vol. 21, no. 9, p. 2097–2100. DOI: 10.1109/LCOMM.2017.2711490
  18. WANG, G., YU, X., TENG, T. Energy-efficient power allocation scheme for uplink distributed antenna system with D2D communication. Mobile Networks and Applications, 2021, vol. 26, p. 1225–1232. DOI: 10.1007/s11036-019-01343-2
  19. HE, C., SHENG, B., ZHU, P., et al. Energy- and spectral efficiency tradeoff for distributed antenna systems with proportional fairness. IEEE Journal on Selected Areas in Communications, 2013, vol. 31, no. 5, p. 894–902. DOI: 10.1109/JSAC.2013.130508
  20. KIM, H., LEE, S., SONG, C., et al. Optimal power allocation scheme for energy efficiency maximization in distributed antenna systems. IEEE Transactions on Communications, 2015, vol. 63, no. 2, p. 431–440. DOI: 10.1109/TCOMM.2014.2385772
  21. LI, X., HE, C., FENG, D., et al. Power allocation criteria for distributed antenna systems with D2D communication. AEU International Journal of Electronics and Communications, 2018, vol. 93, p. 109–115. DOI: 10.1016/j.aeue.2018.05.036
  22. TSENG, P., YUN, S. Block-coordinate gradient descent method for linearly constrained nonsmooth separable optimization. Journal of Optimization Theory and Applications, 2009, vol. 140, p. 513–535. DOI: 10.1007/s10957-008-9458-3
  23. DINKELBACH, W. On nonlinear fractional programming. Management Science, 1967, vol. 13, no. 7, p. 492–498. DOI: 10.1287/mnsc.13.7.492

Keywords: Device-to-Device (D2D) communication, energy efficiency, power allocation, distributed antenna system, rate constraint

I. Jakubova [references] [full-text] [Download Citations]
Brno´s Pilot Smart City Project Spitalka Takes Inspiration from European Lighthouse Cities

Under the EU project RUGGEDISED (Rotterdam, Umea and Glasgow: Generating Exemplar Districts in Sustainable Energy Deployment) several Euro¬pean cities are formulating, implementing and sharing their innovative solutions. As a part of this project, the city of Brno has chosen the unused land of the western part of the heating plant Spitalka to be its replication area and the future pilot smart district. Nowadays Spitalka serves as a testbed for replicating successful solutions and verifying modern technologies and approaches for their possible expansion all over the city. This article considers some of the selected solutions from the RUGGEDISED project and reflects on the conditions for possible replication within the Spitalka project.

  1. advertisement paper, no references

Keywords: no keywords