Muhammad HAROON AURANGZEB, Faisal AKRAM, Imran RASHID, Attiq AHMED [references] [full-text] [DOI: 10.13164/re.2023.0187] [Download Citations]
Pilots Optimization and Surface Area Effects on Channel Estimation in RIS Aided MIMO System

Reconfigurable intelligent surface (RIS) is an emerging tool for 5G and wireless communication technologies that have attracted researchers' interest. However, the passive nature and the high number of reflecting elements in RIS result in a large pilot overhead, which makes channel estimation challenging in multi-user multiple-input multiple-output (MU-MIMO) wireless communication systems. Previous works have shown an improvement in reducing the pilot overhead by exploiting the structured sparsity in rows and columns, which was further improved by compensating offset among users in angular cascaded channels of RIS aided system. To further reduce the pilot overhead, we analyze and adopt coherence-optimized pilots for channel estimation and propose an algorithm to analyze the combined effect of low-coherence pilots with an optimum size of RIS elements for a given number of users, transmit antennas, and normalized error threshold performance. The simulation results illustrate better NMSE performance as compared to contemporary techniques.

1. CISCO. Annual Internet Report (2018–2023) White Paper. 2020, [Online]. Available at: http://www.cisco.com/c/en/us/solutions/collateral/executiveperspectives/annual-internet-report/white-paper-c11-741490.html
2. REPORT LINKER. Global Mobile Data Traffic Industry. 2022, [Online]. Available at: http://www.reportlinker.com/p05442636/GlobalMobile-Data-Traffic-Industry.html
3. BASAR, E., DI RENZO, M., DE ROSNY, J., et al. Wireless communications through reconfigurable intelligent surfaces. IEEE Access, 2019, vol. 7, p. 116753–116773. DOI: 10.1109/ACCESS.2019.2935192
4. LIASKOS, C., NIE, S., TSIOLIARIDOU, A., et al. A new wireless communication paradigm through software-controlled metasurfaces. IEEE Communications Magazine, 2018, vol. 56, no. 9, p. 162–169. DOI: 10.1109/MCOM.2018.1700659
5. RENZO, M. D., DEBBAH, M., PHAN-HUY, D.T., et al. Smart radio environments empowered by reconfigurable AI metasurfaces: An idea whose time has come. EURASIP Journal on Wireless Communications and Networking, 2019, p. 1–20. DOI: 10.1186/s13638-019-1438-9
6. JENSEN, T. L., DE CARVALHO, E. An optimal channel estimation scheme for intelligent reflecting surfaces based on a minimum variance unbiased estimator. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona (Spain), 2020, p. 5000–5004. DOI: 10.1109/ICASSP40776.2020.9053695
7. ZHENG, B., ZHANG, R. Intelligent reflecting surface-enhanced OFDM: Channel estimation and reflection optimization. IEEE Wireless Communications Letters, 2020, vol. 9, no. 4, p. 518–522. DOI: 10.1109/LWC.2019.2961357
8. WANG, Z., LIU, L., CUI, S. Channel estimation for intelligent reflecting surface assisted multiuser communications: Framework, algorithms, and analysis. IEEE Transactions on Wireless Communications, 2020, vol. 19, no. 10, p. 6607–6620. DOI: 10.1109/TWC.2020.3004330
9. WANG, P., FANG, J., DUAN, H., et al. Compressed channel estimation for intelligent reflecting surface-assisted millimeter wave systems. IEEE Signal Processing Letters, 2020, vol. 27, p. 905–909. DOI: 10.1109/LSP.2020.2998357
10. HE, J., WYMEERSCH, H., JUNTTI, M. Channel estimation for RIS-aided mmwave MIMO systems via atomic norm minimization. IEEE Transactions on Wireless Communications, 2021, vol. 20, no. 9, p. 5786–5797. DOI: 10.1109/TWC.2021.3070064
11. CHEN, J., LIANG, Y. C., CHENG, H. V., et al. Channel estimation for reconfigurable intelligent surface aided multi-user MIMO systems. arXiv:1912.03619, 2019, p. 1–16. DOI: 10.48550/arXiv.1912.03619
12. WEI, X., SHEN, D., DAI, L. Channel estimation for RIS assisted wireless communications - Part II: An improved solution based on double structured sparsity. IEEE Communications Letters, 2021, vol. 25, no. 5, p. 1403–1407. DOI: 10.1109/LCOMM.2021.3052787
13. SHI, X., WANG, J., SONG, J. Triple-structured compressive sensingbased channel estimation for RIS-aided MU-MIMO systems. IEEE Transactions on Wireless Communications, 2022, vol. 21, no. 12, p. 1109–11109. DOI: 10.1109/TWC.2022.3189686
14. WU, Q., ZHANG, S., ZHENG, B., et al. Intelligent reflecting surface aided wireless communications: A tutorial. IEEE Transactions on Communications, 2021, vol. 69, no. 5, p. 3313–3351. DOI: 10.1109/TCOMM.2021.3051897
15. TSILIPAKOS, O., TASOLAMPROU, A. C., PITILAKIS, A., et al. Toward intelligent metasurfaces: The progress from globally tunable metasurfaces to software-defined metasurfaces with an embedded network of controllers. Advanced Optical Materials, 2020, vol. 8, no. 17, p. 1–18. DOI: 10.1002/adom.202000783
16. BJORNSON, E., OZDOGAN, O., LARSSON, E. G. Reconfigurable intelligent surfaces: Three myths and two critical questions. IEEE Communications Magazine, 2020, vol. 58, no. 12, p. 90–96. DOI: 10.1109/MCOM.001.2000407
17. BJORNSON, E., WYMEERSCH, H., MATTHIESEN, B., et al. Reconfigurable intelligent surfaces: A signal processing perspective with wireless applications. IEEE Signal Processing Magazine, 2022, vol. 39, no. 2, p. 135–158. DOI: 10.1109/MSP.2021.3130549
18. BJORNSON, E., SANGUINETTI, 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
19. 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
20. HAROON AURANGZEB, M., AKRAM, F., RASHID, I., et al. Sparse RIS in multi user MIMO wireless system. In 16th International Conference on Open Source Systems and Technologies (ICOSST). Lahore (Pakistan), 2022, p. 1–5. DOI: 10.1109/ICOSST57195.2022.10016816
21. MARINOVIC, I., ZANCHI, I., BLAZEVIC, Z. Estimation of channel parameters for "Saleh-Valenzuela" model simulation. In 18th International Conference on Applied Electromagnetics and Communications (ICECOM). Dubrovnik (Croatia), 2005, p. 1–4. DOI: 10.1109/ICECOM.2005.204926
22. WEI, X., SHEN, D., DAI, L. Channel estimation for RIS assisted wireless communications - Part I: Fundamentals, solutions, and future opportunities. IEEE Communications Letters, 2021, vol. 25, no. 5, p. 1398–1402. DOI: 10.1109/LCOMM.2021.3052822
23. DONOHO, D. L. Compressed sensing. IEEE Transactions on Information Theory, 2006, vol. 52, no. 4, p. 1289–1306. DOI: 10.1109/TIT.2006.871582
24. BARANIUK, R. G. Compressive sensing. IEEE Signal Processing Magazine, 2007, vol. 24, no. 4, p. 118–120. DOI: 10.1109/MSP.2007.4286571
25. DUARTE, M. F., ELDAR, Y. C. Structured compressed sensing: From theory to applications.IEEE Transactions on Signal Processing, 2011, vol. 59, no. 9, p. 4053–4085. DOI: 10.1109/TSP.2011.2161982
26. CANDES, E. J., WAKIN, M. B. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008, vol. 25, no. 2, p. 21–30. DOI: 10.1109/MSP.2007.914731
27. AKRAM, F., RASHID, I., GHAFOOR, A., et al. Fast convergence algorithms for coherence optimization of Rank-1 Grassmannian codebooks. Radioengineering, 2019, vol. 28, no. 2, p. 457–463. DOI: 10.13164/re.2019.0456

Keywords: Channel estimation, compressed sensing, reconfigurable intelligent surface, mm-wave mimo communication, sparse channel

L. J. Ge, Z. C. Wang, L. Qian, P. Wei [references] [full-text] [DOI: 10.13164/re.2023.0197] [Download Citations]
Sparsity Adaptive Compressive Sensing based Two-stage Channel Estimation Algorithm for Massive MIMO-OFDM Systems

Massive multi-input multioutput (MIMO) coupled with orthogonal frequency division multiplexing (OFDM) has been utilized extensively in wireless communication systems to investigate spatial diversity. However, the increasing need for channel estimate pilots greatly increases spectrum consumption and signal overhead in massive MIMO-OFDM systems. This paper proposes a two-stage channel estimation algorithm based on sparsity adaptive compressive sensing (CS) to address this issue. To estimate the channel state information (CSI) for pilot locations in Stage 1, we provide a geometry mean-based block orthogonal matching pursuit (GBMP) method. By calculating the geometric mean of the energy in the support set of the channel response, the GBMP method, when compared to conventional CS methods, can drastically reduce the number of iterations and effectively increase the convergence rate of channel reconstruction. Stage 2 involves estimating the CSI for nonpilot locations using a time-frequency correlation interpolation method, which can increase the accuracy of the channel estimation and is dependent on the estimated results from Stage 1. According to the simulation results, the proposed two-stage channel estimation algorithm greatly reduces the running time with little error performance degradation when compared to traditional channel estimating algorithms.

1. PEREIRA DE FIGUEIREDO, F. A. An overview of massive MIMO for 5G and 6G. IEEE Latin America Transactions, 2022, vol. 20, no. 6, p. 931–940. DOI: 10.1109/TLA.2022.9757375
2. MORSALIN, S., MAHMUD, K., TOWN, G. E. Scalability of vehicular M2M communications in a 4G cellular network. IEEE Transactions on Intelligent Transportation Systems, 2018, vol. 19, no. 10, p. 3113–3120. DOI: 10.1109/TITS.2017.2761854
3. LOU, M., JIN, J., WANG, H., et al. Performance analysis of sparse array based massive MIMO via joint convex optimization. China Communications, 2022, vol. 19, no. 3, p. 88–100. DOI: 10.23919/JCC.2022.03.006
4. DONOHO, D. L. Compressed sensing. IEEE Transactions on Information Theory, 2006, vol. 52, no. 4, p. 1289–1306. DOI: 10.1109/TIT.2006.871582
5. CANDES, E. J., WAKIN, M. B. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008, vol. 25, no. 2, p. 21–30. DOI: 10.1109/MSP.2007.914731
6. GAO, Z., DAI, L. L., WANG, Z. C., et al. Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO. IEEE Transactions on Signal Processing, 2015, vol. 63, no. 23, p. 6169–6183. DOI: 10.1109/TSP.2015.2463260
7. GAO, Z., DAI, L. L., DAI, W., et al. Structured compressive sensing based spatio-temporal joint channel estimation for FDD massive MIMO. IEEE Transactions on Communications, 2016, vol. 64, no. 2, p. 601–617. DOI: 10.1109/TCOMM.2015.2508809
8. TAKANO, Y. T., JUNTTI, M. J., MATSUMOTO, T. ℓ1LS and ℓ2MMSE-based hybrid channel estimation for intermittent wireless connections. IEEE Transactions on Wireless Communications, 2016, vol. 15, no. 1, p. 314–328. DOI: 10.1109/TWC.2015.2472418
9. MA, X., YANG, F., LIU, S. C., et al. Structured compressive sensing-based channel estimation for time frequency training OFDM systems over doubly selective channel. IEEE Wireless Communications Letters, 2017, vol. 6, no. 2, p. 266–269. DOI: 10.1109/LWC.2017.2669974
10. HOU, S., WANG, Y. F., ZENG, T. Y., et al. Sparse channel estimation for spatial non-stationary massive MIMO channels. IEEE Communications Letters, 2020, vol. 24, no. 3, p. 681–684. DOI: 10.1109/LCOMM.2019.2961079
11. LAHBIB, N. D., CHERIF, M., HIZEM, M., et al. Channel estimation for TDD uplink massive MIMO systems via compressed sensing. In 15th International Wireless Communications & Mobile Computing Conference (IWCMC). Tangier (Morocco), 2019, p. 1680–1684. DOI: 10.1109/IWCMC.2019.8766722
12. LAHBIB, N. D., CHERIF, M., HIZEM, M., et al. BER analysis and CS-based channel estimation and HPA nonlinearities compensation technique for massive MIMO system. IEEE Access, 2022, vol. 10, p. 27899–27911. DOI: 10.1109/ACCESS.2022.3147353
13. LI, Y., CIMINI, L. J., SOLLENBERGER, N. R. Robust channel estimation for OFDM systems with rapid dispersive fading channels. IEEE Transactions on Communications, 1998, vol. 46, no. 7, p. 902–915. DOI: 10.1109/26.701317
14. DING, M., YANG, X., HU, R., et al. On matrix completion-based channel estimators for massive MIMO systems. Symmetry, 2019, vol. 11, no. 11, p. 1–18. DOI: 10.3390/sym11111377
15. ZHOU, L., ZHAO, J., LU, Y., et al. An improved pilot reuse based estimation method for general channel environment in FDD massive MIMO systems. In 27th Wireless and Optical Communication Conference (WOCC). Hualien (Taiwan), 2018, p. 1–5. DOI: 10.1109/WOCC.2018.8372692
16. CHOI, J. W., LEE, Y. H. Complexity-reduced channel estimation in spatially correlated MIMO-OFDM systems. IEICE Transactions on Communication, 2007, vol. 90, no. 9, p. 2609–2612. DOI: 10.1093/ietcom/e90-b.9.2609
17. AZIZIPOUR, M. J., MOHANED-POUR, K., SWINDLEHURRST, A. L. A burst-form CSI estimation approach for FDD massive MIMO systems. Signal Processing, 2019, vol. 162, p. 106–114. DOI: 10.1016/j.sigpro.2019.04.002
18. JAKES, W. C. Microwave Mobile Communications. Wiley-IEEE Press, 1974. ISBN: 9780470545287. Chapter 1: Multipath Interference, p. 11–78. DOI: 10.1109/9780470545287.ch1
19. LI, Y., SESHADRI, N., ARIYAVISITAKUL, S. Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels. IEEE Journal on Selected Areas in Communications, 1999, vol. 17, no. 3, p. 461–471. DOI: 10.1109/49.753731
20. LI,Y. Simplified channel estimation forOFDMsystems with multiple transmit antennas. IEEE Transactions on Wireless Communications, 2002, vol. 1, no. 1, p. 67–75. DOI: 10.1109/7693.975446
21. DUARTE, M. F., ELDAR, Y. C. Structured compressed sensing: From theory to applications. IEEE Transactions on Signal Processing, 2011, vol. 59, no. 9, p. 4053–4085. DOI: 10.1109/TSP.2011.2161982
22. 206 L. J. GE, Z. C. WANG, L. QIAN, ET AL., SPARSITY ADAPTIVE COMPRESSIVE SENSING BASED TWO-STAGE CHANNEL . . .
23. BJORCK, A. Numerical Methods for Matrix Computations. New York (USA): Springer International Publishing AG, 2014. ISBN: 978-3-319-05088-1
24. DONG, L., ZHAO, H., CHEN, Y., et al. Introduction on IMT-2020 5G trials in China. IEEE Journal on Selected Areas in Communications, 2017, vol. 35, no. 8, p. 1849–1866. DOI: 10.1109/JSAC.2017.2710678
25. BARHUMI, I., LENUS, G., MOONEN, M. Optimal training design for MIMO OFDM systems in mobile wireless channels. IEEE Transactions on Signal Processing, 2003, vol. 51, no. 6, p. 1615–1624. DOI: 10.1109/TSP.2003.811243
26. PENG, W., LI, W., WANG, W., et al. Downlink channel prediction for time-varying FDD massive MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 2019, vol. 13, no. 5, p. 1090–1102. DOI: 10.1109/JSTSP.2019.2931671

Keywords: Channel estimation, compressive sensing, MIMOOFDM, time-frequency correlation

M. Kumar, A. J. Mondal [references] [full-text] [DOI: 10.13164/re.2023.0207] [Download Citations]
An Improved Latch for SerDes Interface: Design and Analysis under PVT and AC Noise

Digital subsystem prefers CMOS process, but it is difficult to manage speed and average power (Pavg) trade-off in each era with power supply voltage (Vdd) scaling. Current mode logic (CML) has emerged as an alternative to design the fundamental block of a SerDes, namely, the latch. However, available CML circuits consume significant Pavg and suffer from rapid input slewing. Typically, fast switching inputs enable current flow to effective supply voltage VP and overcharges output. In fact, VP is different than externally applied Vdd and oscillates with time as and when an abrupt current is drawn. This affects delay td and introduces jitter. The topic presents a new latch for SerDes interface using a new current steering circuit and coupled to a power delivery network (PDN). The significant point is to attain an almost constant td in comparison to conventional designs while the Vdd changes. The post-layout results at 0.09-μm CMOS and 1.1 V Vdd indicate that the Pavg and td are 339.5 µW and 61.9 ps, respectively, at 27OC. Surprisingly, the td variation is noted to be minimum and the power supply noise induced jitter is around 1.5 ns when VP close to the circuit varies due to sudden current.

1. GHILIONI, A., MAZZANTI, A., SVELTO, F. Analysis and design of mm wave frequency dividers based on dynamic latches with load modulation. IEEE Journal of Solid State Circuits, 2013, vol. 48, no. 8, p. 1842–1850. DOI: 10.1109/JSSC.2013.2258793
2. CHANDRAKASAN, A. P., SHENG, S., BRODERSEN, R. W. Low power CMOS digital design. IEEE Journal of Solid State Circuits, 1992, vol. 27, no. 4, p. 473–484. DOI: 10.1109/4.126534
3. RABEY, J. M., CHANDRAKASAN, A., NIKOLIC, B. Digital Integrated Circuits: A Design Perspective. 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2003. ISBN: 978-9332573925
4. NG, H. T., ALLSTOT, D. J. CMOS current steering logic for low-voltage mixed signal circuits. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, 1997, vol. 5, no. 3, p. 301–308. DOI: 10.1109/92.609873
5. HASSAN, H., ANIS, M., ELMASRY, M. MOS current mode circuits: analysis, design and variability. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, 2005, vol. 13, no. 8, p. 885–898. DOI: 10.1109/TVLSI.2005.853609
6. TAPARIA, A., BANERJEE, B., VISWANATHAN, T. R. CS-CMOS: A low noise logic family for mixed signal SoCs. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, 2011, vol. 19, no. 12, p. 2141–2148. DOI: 10.1109/TVLSI.2010.2089812
7. HOSSAIN, M. D. S., SAVIDIS, I. Dynamic differential signaling based logic families for robust ultra-power near threshold computing. Microelectronics Journal, 2020, vol. 102, p. 1–14. DOI: 10.1016/j.mejo.2020.104801
8. BHATTACHARYYA, B. K., LASKAR, N., DEBNATH, S., et al. Innovative scaling method to minimize cost of integrated circuit packages and devices. IEEE Transactions on Component, Packaging and Manufacturing Technology, 2014, vol. 4, no. 9, p. 1489–1494. DOI: 10.1109/TCPMT.2014.2339272
9. ALIOTO, M., MITA, R., PALUMBO, G. Performance evaluation of the low-voltage CML D-latch topology. Integration, 2003, vol. 36, no. 4, p. 191–209. DOI: 10.1016/j.vlsi.2003.09.001
10. HEYDARI, P., MOHANAVELU, R. Design of ultrahigh speed low-voltage CMOS CML buffers and latches. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, 2004, vol. 12, no. 10, p. 1081–1093. DOI: 10.1109/TVLSI.2004.833663
11. PAYANDEHNIA, P., MAGHAMI, H., SHEIKHAEI, S., et al. High speed CML latch using active inductor in 0.18μm CMOS technology. In IEEE 19th Iranian Conference on Electrical Engineering. Tehran (Iran), 2011, p. 1–4. ISSN: 2164-7054
12. TSAI, W. Y., CHIU, C. T., WU, J. M., et al. A novel low-gate count pipeline topology with multiplexer flip-flops for serial links. IEEE Transactions on Circuits and Systems – I: Regular Papers, 2012, vol. 59, no. 11, p. 2600–2610. DOI: 10.1109/TCSI.2012.2206494
13. GUPTA, K., PANDEY, N., GUPTA, M. MCML D latch using triple-tail cells: Analysis and design. Active and Passive Electronic Components, 2013, p. 1–9. DOI: 10.1155/2013/217674
14. SCOTTI, G., BELLIZIA, D., TRIFILETTI, A., et al. Design of low-voltage high-speed CML D latches in nanometer CMOS technologies. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, 2017, vol. 25, no. 12, p. 3509–3520. DOI: 10.1109/TVLSI.2017.2750207
15. YAN, A., HU, Y., CUI, J., et al. Information assurance through redundant design: A novel TNU error resilient latch for harsh radiation environment. IEEE Transactions on Computers, 2020, vol. 69, no. 6, p. 789–799. DOI: 10.1109/TC.2020.2966200
16. SCOTTI, G., TRIFILETTI, A., PALUMBO, G. A novel 0.6V MCML D-latch topology exploiting dynamic body bias threshold lowering. In IEEE 25th International Conference on Electronics, Circuits and Systems. Bordeaux (France), 2018, p. 233–236. DOI: 10.1109/ICECS.2018.8618015
17. KUI, L. F., UDDIN, M. R., MUSYIIRAH, N., et al. Design simulation and analysis of a digital electro-optic SR NOR latch. In TENCON 2018 - 2018 IEEE Region 10 Conference. Jeju (Korea), 2018, p. 2422–2425. DOI: 10.1109/TENCON.2018.8650276
18. AMIRANY, A., RAJAEI, R. Low power and highly reliable single event upset immune latch for nanoscale CMOS technologies. In IEEE Iranian Conference on Electrical Engineering. Mashhad (Iran), 2018, p. 103–107. DOI: 10.1109/ICEE.2018.8472552
19. PIKE, J., PARVIZI, M., BEN-HAMIDA, N., et al. New charge-steering latches in 28nm CMOS for use in high-speed wireline transceiver. In IEEE International Symposium on Circuits and Systems. Florence (Italy), 2018, p. 1–5. DOI: 10.1109/ISCAS.2018.8351013
20. YAN, A., LAI, C., ZHANG, Y., et al. Novel low cost double and triple node upset tolerant latch designs for nano-scale CMOS. IEEE Transactions on Emerging Topics in Computing, 2018, vol. 9, no. 1, p. 520–533. DOI: 10.1109/TETC.2018.2871861
21. KUMAWAT, M., UPADHYAY, A. K., SHARMA, S., et al. An improved current mode logic latch for high-speed applications. International Journal of Communication Systems, 2019, vol. 33, no. 13, p. 1–9. DOI: 10.1002/dac.4118
22. SCOTTI, G., TRIFILETTI, A., PALUMBO, G. A novel 0.5V MCML D flip-flop topology exploiting forward body bias threshold lowering. IEEE. Transactions on Circuits and Systems II: Express Briefs, 2020, vol. 67, no. 3, p. 560–564. DOI: 10.1109/TCSII.2019.2919186
23. SANDHIE, Z. T., AHMED, F. U., CHOWDHURY, M. H. Design of ternary master-slave D flip-flop using MOS-GNRFET. In IEEE International Midwest Symposium on Circuits and Systems. Springfield (MA, USA), 2020, p. 554–557. DOI: 10.1109/MWSCAS48704.2020.9184618
24. KUMAR, M., MONDAL, A. J. A new low power current steering logic circuit for the design of digital subsystem. International Journal of Electronics, 2022, vol. 9, no. 3, p. 497–519. DOI: 10.1080/00207217.2021.1914188

Keywords: PDN, latch, figure of merit, Monte Carlo, output noise, jitter

D. Zhang, X. Chen, S. Qi, H. Zhang [references] [full-text] [DOI: 10.13164/re.2023.0221] [Download Citations]
SIW-Based Frequency-Tunable Self-Oscillating Active Integrated Antenna

A frequency-tunable self-oscillating active integrated antenna (AIA) mainly composed of active circuit and 1×2 substrate integrated waveguide (SIW) antenna array is proposed in this paper. Manipulating bias voltage to the varactors loaded on SIW antenna could offer electronic control of oscillation frequency. The DC bias circuit of the varactors integrated in SIW cavity can provide compact structure. Due to the load effect of the high Q SIW cavity, the designed antenna exhibits low phase noise. According to the measured results, the effective isotropic radiated power (EIRP) ranges from 4.4 to 12.9 dBm which is superior to previous reports with the frequency tuning range of about 20 MHz. The phase noise is -92.7 dBc/Hz at 100 kHz offset. The measured results also show that the cross-polarization levels are almost 20 dB lower than the co-polarized one in the main beam direction at 5.698 GHz.

1. ADHIKARY, M., BISWAS, A., AKHTAR, M. J. Active integrated antenna based permittivity sensing tag. IEEE Sensors Letters, 2017, vol. 1, no. 6, p. 1–4. DOI: 10.1109/LSENS.2017.2768560
2. SHARAWI, M. S., HAMMI, O. Design and Applications of Active Integrated Antennas. London (UK): Artech House, 2018. ISBN: 9781630813581
3. TSAI, Y. L., CHU, H. N., MA, T. G. Self-oscillating circularly polarized active integrated monopole antenna using cross-coupled pair and inverted-l strip. IEEE Antennas and Wireless Propagation Letters, 2020, vol. 19, no. 7, p. 1132–1136. DOI: 10.1109/LAWP.2020.2991467
4. LIN, Y. Y., MA, T. G. Frequency-reconfigurable self-oscillating active antenna with gap-loaded ring radiator. IEEE Antennas and Wireless Propagation Letters, 2013, vol. 12, p. 337–340. DOI: 10.1109/LAWP.2013.2250475
5. WU, C. H., MA, T. G. Pattern-reconfigurable self-oscillating active integrated antenna with frequency agility. IEEE Transactions on Antennas and Propagation, 2014, vol. 62, no. 12, p. 5992–5999. DOI: 10.1109/TAP.2014.2361897
6. LIN, Y. Y., WU, C. H., MA, T. G. Miniaturized self-oscillating annular ring active integrated antennas. IEEE Transactions on Antennas and Propagation, 2011, vol. 59, no. 10, p. 3597–3606. DOI: 10.1109/TAP.2011.2163782
7. WU, C. H., MA, T. G. Self-oscillating semi-ring active integrated antenna with frequency reconfigurability and voltagecontrollability. IEEE Transactions on Antennas and Propagation, 2013, vol. 61, no. 7, p. 3880–3885. DOI: 10.1109/TAP.2013.2256095
8. ADHIKARY, M., SAHOO, S. K., BISWAS, A., et al. SIW-based self-oscillating concurrent dual-frequency active integrated antenna. IEEE Antennas and Wireless Propagation Letters, 2019, vol. 18, no. 9, p. 1897–1901. DOI: 10.1109/LAWP.2019.2932498
9. GIUPPI, F., GEORGIADIS, A., COLLADO, A., et al. A compact, single-layer substrate integrated waveguide (SIW) cavity-backed active antenna oscillator. IEEE Antennas and Wireless Propagation Letters, 2012, vol. 11, p. 431–433. DOI: 10.1109/LAWP.2012.2194470
10. ADHIKARY, M., SARKAR, A., SAHOO, S. K., et al. Half-mode SIW based active integrated circularly polarized leaky wave antenna for automated beam scanning applications. In Proceedings of 2019 IEEE MTT-S International Microwave and RF Conference (IMARC). Mumbai (India), 2019, p. 1–4. DOI: 10.1109/IMaRC45935.2019.9118685
11. GE, L., LI, Y., WANG, J., et al. A low-profile reconfigurable cavity-backed slot antenna with frequency, polarization, and radiation pattern agility. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 5, p. 2182–2189. DOI: 10.1109/TAP.2017.2681432
12. JI, Y., GE, L., WANG, J., et al. Simple beam scanning SIW cavitybacked slot antenna using postloaded varactor. IEEE Antennas and Wireless Propagation Letters, 2019, vol. 18, no. 12, p. 2761–2765. DOI: 10.1109/LAWP.2019.2951447
13. CHANG, Y. W., MA, T. G. Zeroth-order self-oscillating active integrated antenna using cross-coupled pair. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 10, p. 5011–5018. DOI: 10.1109/TAP.2017.2735486
14. CHEN, X., QI, S. S., CHEN, S. L., et al. A low phase noise selfoscillating active antenna. In Proceedings of 2021 International Conference on Microwave and Millimeter Wave Technology (ICMMT). Nanjing (China), 2021, p. 1–3. DOI: 10.1109/ICMMT52847.2021.9618470
15. ASTUTI, D. W., RAHARDJO, E. T. Size reduction of substrate integrated waveguide cavity backed u-slot antenna. In Proceedings of 2018 IEEE Indian Conference on Antennas and Propagation (InCAP). Hyderabad (India), 2018, p. 1–4. DOI: 10.1109/INCAP.2018.8770702

Keywords: Substrate Integrated Waveguide (SIW), Active Integrated Antenna (AIA)

B. N. Tran-Thi, T. T. Nguyen-Ly, T. Hoang [references] [full-text] [DOI: 10.13164/re.2023.0226] [Download Citations]
Further Improvements in Decoding Performance for 5G LDPC Codes Based on Modified Check-Node Unit

One of the most important units of Low-Density Parity-Check (LDPC) decoders is the Check-Node Unit. Its main task is to find the first two minimum values among incoming variable-to-check messages and return check-to-variable messages. This block significantly affects the decoding performance, as well as the hardware implementation complexity. In this paper, we first propose a modification to the check-node update rule by introducing two optimal offset factors applied to the check-to-variable messages. Then, we present the Check-Node Unit hardware architecture which performs the proposed algorithm. The main objective of this work aims to improve further the decoding performance for 5th Generation (5G) LDPC codes. The simulation results show that the proposed algorithm achieves essential improvements in terms of error correction performance. More precisely, the error-floor does not appear within Bit-Error-Rate (BER) of 10^(-8), while the decoding gain increases up to 0.21 dB compared to the baseline Normalized Min-Sum, as well as several state-of-the-art LDPC-based Min-Sum decoders.

1. HAMMING, R. W. Error detecting and error correcting codes. The Bell System Technical Journal, 1950, vol. 29, no. 2, p. 147–160. DOI: 10.1002/j.1538-7305.1950.tb00463.x
2. SHANNON, C. E. A mathematical theory of communication. The Bell System Technical Journal, 1948, vol. 27, no. 3, p. 379–423. DOI: 10.1002/j.1538-7305.1948.tb01338.x
3. CHUNG, S. Y., FORNEY, G. D., RICHARDSON, T. J., et al. On the design of low-density parity-check codes within 0.0045 dB of the Shannon limit. IEEE Communications Letters, 2001, vol. 5, no. 2, p. 58–60. DOI: 10.1109/4234.905935
4. GALLAGER, R. Low-density parity-check codes. IRE Transactions on Information Theory, 1962, vol. 8, no 1, p. 21–28. DOI: 10.1109/TIT.1962.1057683
5. MACKAY, D. J., NEAL, R. M. Near Shannon limit performance of low density parity check codes. Electronics Letters, 1997, vol. 33, no. 6, p. 457–458. DOI: 10.1049/el:19961141
6. TANNER, R. A recursive approach to low complexity codes. IEEE Transactions on Information Theory, 1981, vol. 27, no. 5, p. 533 to 547. DOI: 10.1109/TIT.1981.1056404
7. SUN, H., ZHAO, W., LV, M., et al. Exploiting intracell bit-error characteristics to improve min-sum LDPC decoding for MLC NAND flash-based storage in the mobile device. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, 2016, vol. 24, no. 8, p. 2654–2664. DOI: 10.1109/TVLSI.2016.2535224
8. TSATSARAGKOS, I., PALIOURAS, V. A reconfigurable LDPC decoder optimized for 802.11 n/ac applications. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, 2017, vol. 26, no. 1, p. 182–195. DOI: 10.1109/TVLSI.2017.2752086
9. KIM, S. M., PARK, C. S., HWANG, S. Y. A novel partially parallel architecture for high-throughput LDPC decoder for DVBS2. IEEE Transactions on Consumer Electronics, 2010, vol. 56, no. 2, p. 820–825. DOI: 10.1109/TCE.2010.5506007
10. ANDRADE, J., FALCAO, G., SILVA, V. Flexible design of widepipeline-based WiMAX QC-LDPC decoder architectures on FPGAs using high-level synthesis. Electronics Letters, 2014, vol. 50, no. 11, p. 839–840. DOI: 10.1049/el.2013.3411
11. BALATSOUKAS-STIMMING, A., PREYSS, N., CEVRERO, A., et al. A parallelized layered QC-LDPC decoder for IEEE 802.11 ad. In 2013 IEEE 11th International New Circuits and Systems Conference (NEWCAS). Paris (France), 2013, p. 1–4. DOI: 10.1109/NEWCAS.2013.6573590
12. MYUNG, S., PARK, S. I., KIM, K. J., et al. Offset and normalized min-sum algorithms for ATSC 3.0 LDPC decoder. IEEE Transactions on Broadcasting, 2017, vol. 63, no. 4, p. 734–739. DOI: 10.1109/TBC.2017.2686011
13. FOSSORIER, M. P. C. Quasicyclic low-density parity-check codes from circulant permutation matrices. IEEE Transactions on Information Theory, 2004, vol. 50, no. 8, p. 1788–1793. DOI: 10.1109/TIT.2004.831841
14. LI, J., LIU, K., LIN, S., et al. Decoding of quasi-cyclic LDPC codes with section-wise cyclic structure. In Proceedings of the IEEE Information Theory and Applications Workshop (ITA'14). San Diego (CA, USA), 2014, p. 1–10. DOI: 10.1109/ITA.2014.6804221
15. CAI, F., ZHANG, X., DECLERCQ, D., et al. Finite alphabet iterative decoders for LDPC codes: Optimization, architecture and analysis. IEEE Transactions on Circuits and Systems I: Regular Papers, 2014, vol. 61, no. 5, p. 1366–1375. DOI: 10.1109/TCSI.2014.2309896
16. LI, Z., CHEN, L., ZENG, L., et al. Efficient encoding of quasicyclic low-density parity-check codes. IEEE Transactions on Communications, 2006, vol. 54, no. 1, p. 71–81. DOI: 10.1109/TCOMM.2005.861667
17. LIU, H., HUANG, Q., DENG, G., et al. Quasi-cyclic representation and vector representation of RS-LDPC Codes. IEEE Transactions on Communications, 2015, vol. 63, no. 4, p. 1033 to 1042. DOI: 10.1109/TCOMM.2015.2399395
18. JIANG, N., PENG, K., SONG, J., et al. High-throughput QCLDPC decoders. IEEE Transactions on Broadcasting, 2009, vol. 55, no. 2, p. 251–259. DOI: 10.1109/TBC.2008.2012359
19. CHANG, D., YU, F., XIAO, Z., et al. FPGA verification of a single QC-LDPC code for 100 Gb/s optical systems without error floor down to BER of 10−15. In Optical Fiber Communication Conference (p. OTuN2), Optical Society of America. Los Angeles (USA), 2011. DOI: 10.1364/OFC.2011.OTuN2
20. THI BAO NGUYEN, T., NGUYEN TAN, T., LEE, H. Lowcomplexity high-throughput QC-LDPC decoder for 5G new radio wireless communication. Electronics, 2021, vol. 10, no. 4, p. 1–18. DOI: 10.3390/electronics10040516
21. MA, L., CHOU, H. F., SHAM, C. W. A novel data packing technique for QC-LDPC decoder architecture applied to NAND flash controller. In 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). Osaka (Japan), 2019, p. 897–898. DOI: 10.1109/GCCE46687.2019.9015393
22. RICHARDSON, T., KUDEKAR, S. Design of low-density paritycheck codes for 5G new radio. IEEE Communications Magazine, 2018, vol. 56, no. 3, p. 28–34. DOI: 10.1109/MCOM.2018.1700839
23. ETSI. 5G; NR; Multiplexing and Channel Coding (Release 15), document 3GPP TS 38.212, V15.2.0, 2018. [Online] Cited 2022-07-31. Available at: https://www.etsi.org/deliver/etsi_ts/138200_138299/138212/15.02.00_60/ts_138212v150200p.pdf
24. MAUNDER, R. G. The 5G Channel Code Contenders. ACCELERCOMM White Paper, 2016, p. 1–13.
25. LUBY, M. G., MITZENMACHER, M., SHOKROLLAHI, M. A., et al. Improved low-density parity-check codes using irregular graphs. IEEE Transactions on Information Theory, 2001, vol. 47, no. 2, p. 585–598. DOI: 10.1109/18.910576
26. JOSE, R., PE, A. Analysis of hard decision and soft decision decoding algorithms of LDPC codes in AWGN. In 2015 IEEE International Advance Computing Conference (IACC). Bangalore (India), 2015, p. 430–435. DOI: 10.1109/IADCC.2015.7154744
27. RICHARDSON, T. J., URBANKE, R. L. The capacity of lowdensity parity-check codes under message-passing decoding. IEEE Transactions on Information Theory, 2001, vol. 47, no. 2, p. 599 to 618. DOI: 10.1109/18.910577
28. FOSSORIER, M. P. C, MIHALJEVIC, M., IMAI, H. Reduced complexity iterative decoding of low-density parity-check codes based on belief propagation. IEEE Transactions on Communications, 1999, vol. 47, no. 5, p. 673–680. DOI: 10.1109/26.768759
29. CHEN, J., DHOLAKIA, A., ELEFTHERIOU, E., et al. Reducedcomplexity decoding of LDPC codes. IEEE Transactions on Communications, 2005, vol. 53, no. 8, p. 1288–1299. DOI: 10.1109/TCOMM.2005.852852
30. DARABIHA, A., CARUSONE, A. C., KSCHISCHANG, F. R. A bit-serial approximate min-sum LDPC decoder and FPGA implementation. In 2006 IEEE International Symposium on Circuits and Systems. Kos (Greece), 2006, p. 149–152. DOI: 10.1109/ISCAS.2006.1692544
31. ANGARITA, F., VALLS, J., ALMENAR, V., et al. Reducedcomplexity min-sum algorithm for decoding LDPC codes with low error-floor. IEEE Transactions on Circuits and Systems I: Regular Papers, 2014, vol. 61, no. 7, p. 2150–2158. DOI: 10.1109/TCSI.2014.2304660
32. CHO, K., LEE, W. H., CHUNG, K. S. Simplified 2-dimensional scaled min-sum algorithm for LDPC decoder. Journal of Electrical Engineering &Technology, 2017, vol. 12, no. 3, p. 1262–1270. DOI: 10.5370/JEET.2017.12.3.1262
33. CATALÀ-PEREZ, J. M., LACRUZ, J. O., GARCIA-HERRERO, F., et al. Second minimum approximation for min-sum decoders suitable for high-rate LDPC codes. Circuits, Systems, and Signal Processing, 2019, vol. 38, no. 11, p. 5068–5080. DOI: 10.1007/s00034-019-01107-z
34. CUI, H., GHAFFARI, F., LE, K., et al. Design of highperformance and area-efficient decoder for 5G LDPC codes. IEEE Transactions on Circuits and Systems I: Regular Papers, 2020, vol. 68, no. 2, p. 879–891. DOI: 10.1109/TCSI.2020.3038887
35. TRAN-THI, B. N., NGUYEN-LY, T. T., HONG, H. N., et al. An improved offset min-sum LDPC decoding algorithm for 5G new radio. In 2021 International Symposium on Electrical and Electronics Engineering (ISEE). Ho Chi Minh City (Vietnam), 2021, p. 106–109. DOI: 10.1109/ISEE51682.2021.9418782
36. ETSI. LTE; Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (EUTRAN). 3GPP TS 36.300, V11.6.0, 2013. [Online] Cited 2022-07-31. Available at: https://www.etsi.org/deliver/etsi_ts/136300_136399/136300/11.06.00_60/ts_136300v110600p.pdf
37. AHN, S. K., KIM, K. J., MYUNG, S., et al. Comparison of lowdensity parity-check codes in ATSC 3.0 and 5G standards. IEEE Transactions on Broadcasting, 2019, vol. 65, no. 3, p. 489–495. DOI: 10.1109/TBC.2018.2874541
38. HUI, D., SANDBERG, S., BLANKENSHIP, Y., et al. Channel coding in 5G new radio: A tutorial overview and performance comparison with 4G LTE. IEEE Vehicular Technology Magazine, 2018, vol. 13, no. 4, p. 60–69. DOI: 10.1109/MVT.2018.2867640
39. 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
40. CUI, H., LE TRUNG, K., GHAFFARI, F., et al. An enhanced offset min-sum decoder for 5G LDPC codes. In 2019 25th AsiaPacific Conference on Communications (APCC). Ho Chi Minh City (Vietnam), 2019, p. 490–495. DOI: 10.1109/APCC47188.2019.9026399
41. WEY, C. L., SHIEH, M. D., LIN, S. Y. Algorithms of finding the first two minimum values and their hardware implementation. IEEE Transactions on Circuits and Systems I: Regular Papers, 2008, vol. 55, no. 11, p. 3430–3437. DOI: 10.1109/TCSI.2008.924892
42. LEE, Y., KIM, B., JUNG, J., et al. Low-complexity tree architecture for finding the first two minima. IEEE Transactions on Circuits and Systems II: Express Briefs, 2015, vol. 62, no. 1, p. 61–64. DOI: 10.1109/TCSII.2014.2362663

Keywords: Bit error rate, CNU architecture, LDPC codes, low computational complexity, Min-Sum algorithm, Normalized Min-Sum

M. Y. Onay, O. Ertug [references] [full-text] [DOI: 10.13164/re.2023.0236] [Download Citations]
Ambient Backscatter Communication Based Cooperative Relaying for Heterogeneous Cognitive Radio Networks

In this paper, a new network model is proposed to improve the performance of the secondary channel in cognitive radio networks (CRNs) based ambient backscatter communication systems. This model is considered as a cooperative system with multi-secondary transmitter (ST) and multi-relay. The ST backscatters data to both the secondary receiver (SR) and relay. Also it harvests energy from the signal emitted by the primary transmitter (PT) during the busy period. The relay activated by the ST user forwards the information from ST to SR. During the idle period, the PT broadcast is interrupted and ST also performs active data transmission using the energy it has harvested. We aim to maximize the number of data transmitted to the SR. Therefore, how long the ST will perform backscattering, energy harvesting and active data transmission is a problem to be solved. In such cooperative systems with multiple users, the solution of the problem becomes more complex. Therefore, the system model has been mathematically modeled and transformed into an optimization problem, considering that users are transmitting data using time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) techniques. Numerical results showed that higher data rates were achieved in NOMA. Additionally, It has been seen that the proposed model performs better when compared to the existing approaches in the literature, where the ST can only harvest energy and transmit data actively or only transmit data with ambient backscatter communication.

1. AKYILDIZ, I. F., KAK, A., NIE, S. 6G and Beyond: The future of wireless communications systems. IEEE Access, 2020, vol. 8, p. 133995–134030. DOI: 10.1109/ACCESS.2020.3010896
2. WU, W., WANG, X., HAWBANI, A., et al. A survey on ambient backscatter communications: Principles, systems, applications, and challenges. Computer Networks, 2022, vol. 216, p. 1–17. DOI: 10.1016/j.comnet.2022.109235
3. JU, H., ZHANG, R. Throughput maximization in wireless powered communication networks. IEEE Transactions on Wireless Communications, 2014, vol. 13, no. 1, p. 418–428. DOI: 10.1109/TWC.2013.112513.130760
4. LIU, V., PARKS, A., TALLA, V., et al. Ambient backscatter: Wireless communication out of thin air. Association for Computing Machinery, 2013, vol. 43, no. 4, p. 39–50. DOI: 10.1145/2534169.2486015
5. XU, C., YANG, L., ZHANG, P. Practical backscatter communication systems for battery-free internet of things: A tutorial and survey of recent research. IEEE Signal Processing Magazine, 2018, vol. 35, no. 5, p. 16–27. DOI: 10.1109/MSP.2018.2848361
6. HUYNH, N. V., HOANG, D. T., LU, X., et al. Ambient backscatter communications: A contemporary survey. IEEE Communications Surveys Tutorials, 2018, vol. 20, no. 4, p. 2889–2922. DOI: 10.1109/COMST.2018.2841964
7. LIU, X., GAO, Y., HU, F. Optimal time scheduling scheme for wireless powered ambient backscatter communications in IoT networks. IEEE Internet of Things Journal, 2019, vol. 6, no. 2, p. 2264–2272. DOI: 10.1109/JIOT.2018.2889700
8. MURATKAR, T. S., BHURANE, A., SHARMA, P., et al. Ambient backscatter communication with mobile RF source for IoT-based applications. AEU - International Journal of Electronics and Communications, 2021, vol. 141, p. 1–11. DOI: 10.1016/j.aeue.2021.153974
9. ONAY, M. Y., DULEK, B. Performance analysis of TV, FM and WiFi signals in backscatter communication networks. In 27th Signal Processing and Communications Applications Conference (SIU). Sivas (Turkey), 2019, p. 1–4. DOI: 10.1109/SIU.2019.8806350
10. LYU, B., YANG, Z., GUI, G., et al. Wireless powered communication networks assisted by backscatter communication. IEEE Access, 2017, vol. 5, p. 7254–7262. DOI: 10.1109/ACCESS.2017.2677521
11. HOANG, D. T., NIYATO, D., WANG, P., et al. Ambient backscatter: A new approach to improve network performance for RF-Powered cognitive radio networks. IEEE Transactions on Communication, 2017, vol. 65, no. 9, p. 3659–3674. DOI: 10.1109/TCOMM.2017.2710338
12. KISHORE, R., GURUGOPINATH, S., SOFOTASIOS, P. C., et al. Opportunistic ambient backscatter communication in RF-powered cognitive radio networks. IEEE Transactions on Cognitive Communications and Networking, 2019, vol. 5, no. 2, p. 413–426. DOI: 10.1109/TCCN.2019.2907090
13. LI, Q. Sum-throughput maximization in backscatter communicationbased cognitive networks. Wireless Communications and Mobile Computing, 2022, vol. 2022, p. 1–11. DOI: 10.1155/2022/7768588
14. ONAY, M. Y., DURAK, M. H., ERTUG, O. Transmission performance analysis of cognitive radio based backscatter communication systems. In 13th International Conference on Electrical and Electronics Engineering (ELECO). Bursa (Turkey), 2021, p. 1–5. DOI: 10.23919/ELECO54474.2021.9677814
15. HOANG, D. T., NIYATO, D., WANG, P., et al. Optimal time sharing in RF-powered backscatter cognitive radio networks. In IEEE International Conference on Communications (ICC). Paris (France), 2017, p. 1–6. DOI: 10.1109/ICC.2017.7996410
16. LYU, B. T., YANG, Z., XIE, T., et al. Optimal time allocation in relay assisted backscatter communication systems. In IEEE 87th Vehicular Technology Conference (VTC Spring). Porto (Portugal), 2018, p. 1–5. DOI: 10.1109/VTCSpring.2018.8417655
17. GAO, X., NIYATO, D., YANG, K., et al. Cooperative scheme for backscatter-aided passive relay communications in wireless-powered D2D networks. IEEE Internet of Things Journal, 2022, vol. 9, no. 1, p. 152–164. DOI: 10.1109/JIOT.2021.3096652
18. WANG, W.-J., XU, K., YAN, Y., et al. Relay selection-based cooperative backscatter transmission with energy harvesting: Throughput maximization. IEEE Wireless Communications Letters, 2022, vol. 11, no. 7, p. 1533–1537. DOI: 10.1109/LWC.2022.3179019
19. ZHENG, C., ZHOU, W., LU, X. Energy efficiency maximization in the wireless-powered backscatter communication networks with DF relaying. Wireless Communications and Mobile Computing, 2022, vol. 2022, p. 1–12. DOI: 10.1155/2022/2806423
20. CHEN, H., LI, Y., REBELATTO, J. L., et al. Harvest-then-cooperate: Wireless-powered cooperative communications. IEEE Transactions on Signal Processing, 2015, vol. 63, no. 7, p. 1700–1711. DOI: 10.1109/TSP.2015.2396009
21. LI, D. Backscatter communication via harvest-then-transmit relaying. IEEE Transactions on Vehicular Technology, 2020, vol. 69, p. 6843–6847. DOI: 10.1109/TVT.2020.2991227
22. SIROJUDDIN, A., NZIMA, V., SINGH, K., et al. Backscatteraided relaying for next-generation wireless communications with SWIPT. IEEE Access, 2021, vol. 9, p. 159093–159104. DOI: 10.1109/ACCESS.2021.3131211
23. HUSSAIN, Q., SOHAIB, S. Full duplex relaying in non orthogonal multiple access system with advanced successive interference cancellation. Radioengineering, 2020, vol. 29, p. 654–663. DOI: 10.13164/re.2020.0654
24. YUNIDA, Y., MUHARAR, R., AWAY, Y., et al. Efficient relay selection algorithm for non-orthogonal amplify-and-forward cooperative systems over block-fading channels. Radioengineering, 2020, vol. 29, p. 386–396. DOI: 10.13164/re.2020.0386
25. NGUYEN, K.-T., DO, D.-T., VOZNAK, M. An optimal analysis in wireless powered full-duplex relaying network. Radioengineering, 2017, vol. 26, p. 369–375. DOI: 10.13164/re.2017.0369
26. BLETSAS, A., ALEVIZOS, P. N., VOUGIOUKAS, G. The art of signal processing in backscatter radio for μW (or less) internet of things: Intelligent signal processing and backscatter radio enabling batteryless connectivity. IEEE Signal Processing Magazine, 2018, vol. 35, no. 5, p. 28–40. DOI: 10.1109/MSP.2018.2837678
27. KIM, S. H., KIM, D. I. Hybrid backscatter communication for wireless-powered heterogeneous networks. IEEE Transactions on Wireless Communications, 2017, vol. 16, no. 10, p. 6557–6570. DOI: 10.1109/TWC.2017.2725829
28. SUN, J., ZHANG, S., CHI, K. Optimal time allocation for throughput maximization in backscatter assisted wireless powered communication networks. IET Communications, 2021, vol. 15, no. 12, p. 1620–1631. DOI: 10.1049/cmu2.12175
29. DIAMANTOULAKIS, P. D., PAPPI, K. N., DING, Z., et al. Wirelesspowered communications with non-orthogonal multiple access. IEEE Transactions on Wireless Communication, 2016, vol. 15, no. 12, p. 8422–8436. DOI: 10.1109/TWC.2016.2614937
30. YANG, G., XU, X., LIANG, Y.-C. Resource allocation in NOMAenhanced backscatter communication networks for wireless powered IoT. IEEE Wireless Communications Letters, 2020, vol. 9, no. 1, p. 117–120. DOI: 10.1109/LWC.2019.2944369
31. LYU, B. T., YANG, Z., GUI, G., et al. Optimal time allocation in backscatter assisted wireless powered communication networks. Sensors, 2017, vol. 17, no. 6, p. 1–11. DOI: 10.3390/s17061258
32. LU, X., NIYATO, D., JIANG, H., et al. Ambient backscatter assisted wireless powered communications. IEEE Wireless Communications, 2018, vol. 25, no. 2, p. 170–177. DOI: 10.1109/MWC.2017.1600398
33. COSTA, M., EPHREMIDES, A. Energy efficiency versus performance in cognitive wireless networks. IEEE Journal on Selected Areas in Communications, 2016, vol. 34, no. 5, p. 1336–1347. DOI: 10.1109/JSAC.2016.2520219
34. MILI, M. R., MUSAVIAN, L., HAMDI, K. A., et al. How to increase energy efficiency in cognitive radio networks. IEEE Transactions on Communications, 2016, vol. 64, no. 5, p. 1829–1843. DOI: 10.1109/TCOMM.2016.2535371
35. HU, H., ZHANG, H., LIANG, Y.-C. On the spectrum- and energyefficiency tradeoff in cognitive radio networks. IEEE Transactions on Communications, 2016, vol. 64, no. 2, p. 490–501. DOI: 10.1109/TCOMM.2015.2505281

Keywords: Ambient backscatter communication, cognitive radio networks, cooperative system, relay, energy harvesting, convex optimization

K. Chen, M. Gu, Z. Chen [references] [full-text] [DOI: 10.13164/re.2023.0248] [Download Citations]
Radar-Based Human Motion Recognition by Using Vital Signs with ECA-CNN

Radar technologies reserve a large latent capacity in dealing with human motion recognition (HMR). For the problem that it is challenging to quickly and accurately classify various complex motions, an HMR algorithm combing the attention mechanism and convolution neural network (ECA-CNN) using vital signs is proposed. Firstly, the original radar signal is obtained from human chest wall displacement. Chirp-Z Transform (CZT) algorithm is adopted to refine and amplify the narrow band spectrum region of interest in the global spectrum of the signal, and accurate information on the specific band is extracted. Secondly, six time-domain features were extracted for the neural network. Finally, an ECA-CNN is designed to improve classification accuracy, with a small size, fast speed, and high accuracy of 98%. This method can improve the classification accuracy and efficiency of the network to a large extent. Besides, the size of this network is 100 kb, which is convenient to integrate into the embedded devices.

1. BAO, J. B., ZHOU, L., LIU, G.H., et al. Current state of care for the elderly in China in the context of an aging population. BioScience Trends, 2022, vol. 16, no. 2, p. 107–118. DOI: 10.5582/bst.2022.01068
2. ROBLEDO, L. M. G., CANO-GUTIERREZ, C., GARCIA, E. V. Healthcare for older people in Central and South America. Age and Ageing, 2022, vol. 51, no. 5, p. 1–4. DOI: 10.1093/ageing/afac017
3. PIRZADA, P., WILDE, A., DOHERTY, G. H., et al. Ethics and acceptance of smart homes for older adults. Informatics for Health and Social Care, 2021, vol. 47, no. 1, p. 10–37. DOI: 10.1080/17538157.2021.1923500
4. PHILIP, N. Y., RODRIGUES, J. J. P. C., WANG, H. G., et al. Internet of Things for in-home health monitoring systems: Current advances, challenges and future directions. IEEE Journal on Selected Areas in Communications, 2021, vol. 39, no. 2, p. 300–310. DOI: 10.1109/JSAC.2020.3042421
5. PHAM, M., YANG, D., SHENG, W. H. A sensor fusion approach to indoor human localization based on environmental and wearable sensors. IEEE Transactions on Automation Science and Engineering, 2019, vol. 16, no. 1, p. 339–350. DOI: 10.1109/TASE.2018.2874487
6. NASIRI, S., KHOSRAVANI, M. R. Progress and challenges in fabrication of wearable sensors for health monitoring. Sensors and Actuators A: Physical, 2020, vol. 312, p. 1–17. DOI: 10.1016/j.sna.2020.112105
7. PENG, D. B., LIU, Y. H. Wireless sensor acquisition of human motion parameters based on blockchain. Journal of Sensors, 2021, p. 1–13. DOI: 10.1155/2021/4564143
8. LIU, Z. B., HUANG, J. X., HAN, J. W., et al. Human motion tracking by multiple RGBD cameras. IEEE Transactions on Circuits and Systems for Video Technology, 2017, vol. 27, no. 9, p. 2014–2027. DOI: 10.1109/TCSVT.2016.2564878
9. YAN, B. J., ZHANG, H., YAO, Y. C., et al. Heart signatures: Openset person identification based on cardiac radar signals. Biomedical Signal Processing and Control, 2022, vol. 72, p. 1–12. DOI: 10.1016/j.bspc.2021.103306
10. WANG, M. Y., ZHANG, Y. M., CUI, G. L. Human motion recognition exploiting radar with stacked recurrent neural network. Digital Signal Processing, 2019, vol. 87, p. 125–131. DOI: 10.1016/j.dsp.2019.01.013
11. ZHANG, R. Y., CAO, S. Y. Real-time human motion behavior detection via CNN using mm-wave radar. IEEE Sensors Letters, 2019, vol. 3, no. 2, p. 1–4. DOI: 10.1109/LSENS.2018.2889060
12. MAITRE, J., BOUCHARD, K., BERTUGLIA, C., et al. Recognizing activities of daily living from UWB radars and deep learning. Expert Systems with Applications, 2021, vol. 164, p. 1–13. DOI: 10.1016/J.ESWA.2020.113994
13. SHEN, H. M., XU, C., YANG, Y. J., et al. Respiration and heartbeat rates measurement based on autocorrelation using IR-UWB radar. IEEE Transactions on Circuits and Systems II: Express Briefs, 2018, vol. 65, no. 10, p. 1470–1474. DOI: 10.1109/TCSII.2018.2860015
14. GOUVEIA, C., TOME, A., BARROS, F., et al. Study on the usage feasibility of continuous-wave radar for emotion recognition. Biomedical Signal Processing and Control, 2020, vol. 58, p. 1–10. DOI: 10.1016/j.bspc.2019.101835
15. YEN, H. Y., KUROSAWA, M., KIRIMOTO, T., et al. A medical radar system for non-contact vital sign monitoring and clinical performance evaluation in hospitalized older patients. Biomedical Signal Processing and Control, 2022, vol. 75, p. 1–12. DOI: 10.1016/j.bspc.2022.103597
16. WU, W. H., STASZEWSKI, R. B., LONG, J. R. A 56.4-to-63.4 GHz multi-rate all-digital fractional-N PLL for FMCW radar applications in 65 nm CMOS. IEEE Journal of Solid-State Circuits, 2014, vol. 49, no. 5, p. 1081–1096. DOI: 10.1109/JSSC.2014.2301764
17. VANDERSMISSEN, B., KNUDDE, N., JALALVAND, A., et al. Indoor person identification using a low-power FMCW radar. IEEE Transactions on Geoscience and Remote Sensing, 2018, vol. 56, no. 7, p. 3941–3952. DOI: 10.1109/TGRS.2018.2816812
18. ALIZADEH, M., SHAKER, G., DE ALMEIDA, J. C. M., et al. Remote monitoring of human vital signs using mm-wave FMCW radar. IEEE Access, 2019, vol. 7, p. 54958–54968. DOI: 10.1109/ACCESS.2019.2912956
19. DING, C. W., ZHANG, L., GU, C., et al. Non-contact human motion recognition based on UWB radar. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2018, vol. 8, no. 2, p. 306–315. DOI: 10.1109/JETCAS.2018.2797313
20. WANG, D. Y., YOO, S. W., CHO, S. H. Experimental comparison of IR-UWB radar and FMCW radar for vital signs. Sensors, 2020, vol. 20, p. 1–22. DOI: 10.3390/s20226695
21. LOPES, A., NORONHA OSORIO, D. F., SILVA, H. G., et al. Equivalent pipeline processing for IR-UWB and FMCW radar comparison in vital signs monitoring applications. IEEE Sensors Journal, 2022, vol. 22, no. 12, p. 12028–12035. DOI: 10.1109/jsen.2022.3173218
22. PENG, Z. Y., MUNOZ-FERRERAS, J. M., TANG, Y., et al. A portable FMCW interferometry radar with programmable low-IF architecture for localization, ISAR imaging, and vital sign tracking. IEEE Transactions on Microwave Theory and Techniques, 2017, vol. 65, no. 4, p. 1334–1344. DOI: 10.1109/TMTT.2016.2633352
23. HE, M., NIAN, Y. J., GONG, Y. S. Novel signal processing method for vital sign monitoring using FMCW radar. Biomedical Signal Processing and Control, 2017, vol. 33, p. 335–345. DOI: 10.1016/j.bspc.2016.12.008
24. LEE, H., KIM, B. H., PARK, J. K., et al. A novel vital-sign sensing algorithm for multiple subjects based on 24-GHz FMCW Doppler radar. Remote Sensing, 2019, vol. 11, no. 10, p. 1–15. DOI: 10.3390/rs11101237
25. MERCURI, M., LU, Y. T., POLITO, S., et al. Enabling robust radar-based localization and vital signs monitoring in multipath propagation environments. IEEE Transactions on Biomedical Engineering, 2021, vol. 68, no. 11, p. 3228–3240. DOI: 10.1109/TBME.2021.3066876
26. YANG, Z. T., QIU, W., SUN, H. J., et al. Robust radar emitter recognition based on the three-dimensional distribution feature and transfer learning. Sensors, 2016, vol. 16, no. 3, p. 1–14. DOI: 10.3390/s16030289
27. LIU, S. K., YAN, X. P., LI, P., et al. Radar emitter recognition based on SIFT position and scale features. IEEE Transactions on Circuits and Systems II: Express Briefs, 2018, vol. 65, no. 12, p. 2062–2066. DOI: 10.1109/TCSII.2018.2819666
28. SAKAMOTO, T., AUBRY, P. J., OKUMURA, S., et al. Noncontact measurement of the instantaneous heart rate in a multi-person scenario using X-band array radar and adaptive array processing. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2018, vol. 8, no. 2, p. 280–293. DOI: 10.1109/JETCAS.2018.2809582
29. CHIAN, D. M., WEN, C. L., WANG, F. K., et al. Signal separation and tracking algorithm for multi-person vital signs by using Doppler radar. IEEE Transactions on Biomedical Circuits and Systems, 2020, vol. 14, no. 6, p. 1346–1361. DOI: 10.1109/TBCAS.2020.3029709
30. NEEMAT, S., KRASNOV, O., YAROVOY, A. An interference mitigation technique for FMCW radar using beat-frequencies interpolation in the STFT domain. IEEE Transactions on Microwave Theory and Techniques, 2019, vol. 67, no. 3, p. 1207–1220. DOI: 10.1109/TMTT.2018.2881154
31. LI, C. Z., UN, K. F., MAK, P. I., et al. Overview of recent development on wireless sensing circuits and systems for healthcare and biomedical applications. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2018, vol. 8, no. 2, p. 165–177. DOI: 10.1109/JETCAS.2018.2822684
32. KIM, Y., MOON, T. Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 2016, vol. 13, no. 1, p. 8–12. DOI: 10.1109/LGRS.2015.2491329
33. EROL, B., AMIN, M. G. Radar data cube analysis for fall detection. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Calgary (Canada), 2018, p. 2446–2450. DOI: 10.1109/ICASSP.2018.8461512
34. TRAN, N., KILIC, O., NAHAR, S., et al. Contactless monitoring and classification of human motion activities by using SFCW radar. In IEEE International Symposium on Antennas and Propagation (APSURSI). Fajardo (Puerto Rico, USA), 2016, p. 883–884. DOI: 10.1109/APS.2016.7696150
35. ANWAR, S. M., MAJID, M., QAYYUM, A., et al. Medical image analysis using convolutional neural networks: A review. Journal of Medical Systems, 2018, vol. 42, no. 11, p. 1–13. DOI: 10.1007/s10916-018-1088-1
36. WANG, Q. L., WU, B. G., ZHU, P. F., et al. ECA-net: Efficient channel attention for deep convolutional neural networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Boston (USA), 2020, p. 11531–11539. DOI: 10.1109/CVPR42600.2020.01155

Keywords: Human motion recognition, vital signs, Efficient Channel Attention enabled Convolutional Neural Network (ECA-CNN), radar

B. Velichkovska, A. Cholakoska, V. Atanasovski [references] [full-text] [DOI: 10.13164/re.2023.0256] [Download Citations]
Machine Learning Based Classification of IoT Traffic

With the rapid expansion and widespread adoption of the Internet of Things (IoT), maintaining secure connections among active devices can be challenging. Since IoT devices are limited in power and storage, they cannot perform complex tasks, which makes them vulnerable to different types of attacks. Given the volume of data generated daily, detecting anomalous behavior can be demanding. However, machine learning (ML) algorithms have proven successful in extracting complex patterns from big data, which has led to active applications in IoT. In this paper, we perform a comprehensive analysis, including 4 ML algorithms and 3 neural networks (NNs), and propose a pipeline which analyzes the influence data reduction (loss) has on the performance of these algorithms. We use random undersampling as a data reduction technique, which simulates reduced network traffic data. The pipeline investigates several degrees of data loss. The results show that models trained on the original data distribution obtain accuracy that verges on 100%. XGBoost performs best from the classic ML algorithms. From the deep learning models, the 2-layered NN provides excellent results and has sufficient depth for practical application. On the other hand, when the models are trained on the undersampled data, there is a decrease in performance, most notably in the case of NNs. The most prominent change is seen in the 4-layered NN, where the model trained on the original dataset detects attacks with a success of 93.53%, whereas the model trained on the maximally reduced data has a success of only 39.39%.

1. ASKARAN, KHAN, N., NANDINI, et al. IOT: Applications, challenges and latest trends. 1st IEEE International Conference on Industrial Electronics: Developments & Applications (ICIDeA). Bhubaneswar (India), 2022, p. 181–186. DOI: 10.1109/ICIDeA53933.2022.9970100
2. GUPTA, S., TANWAR, S., GUPTA, N. A systematic review on internet of things (IoT): Applications & challenges. 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). Noida (India), 2022, p. 1–7. DOI: 10.1109/ICRITO56286.2022.9964892
3. HUSSEIN, D. H., IBNKAHLA, M. An IoT traffic modeling framework and its application to autonomous edge scaling. IEEE Global Communications Conference (GLOBECOM). Rio de Janeiro (Brazil), 2022, p. 5656–5661. DOI: 10.1109/GLOBECOM48099.2022.10000950
4. THAMILARASU, G., ODESILE, A., HOANG, A. An intrusion detection system for internet of medical things. IEEE Access, 2020, vol. 8, p. 181560–181576. DOI: 10.1109/ACCESS.2020.3026260
5. PRADHAN, M., MOHANTY, S., SEEMONA, A. O. Machine learning-based intrusion detection system for the internet of vehicles. In 5th International Conference on Computational Intelligence and Networks (CINE). Bhubaneswar (India), 2022, p. 1–6. DOI: 10.1109/CINE56307.2022.10037357
6. LI, S., LU, Y., LI, J. CAD-IDS: A cooperative adaptive distributed intrusion detection system with fog computing. In IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD). Hangzhou (China), 2022, p. 635–640. DOI: 10.1109/CSCWD54268.2022.9776147
7. NASSIF, A., TALIB, M., NASIR, Q., et al. Machine learning for anomaly detection: A systematic review. IEEE Access, 2021, vol. 9, p. 78658–78700. DOI: 10.1109/ACCESS.2021.3083060
8. AMARUDIN, FERDIANA, R., WIDYAWAN. A systematic literature review of intrusion detection system for network security: Research trends, datasets and methods. In Proceedings of the 4th International Conference On Informatics And Computational Sciences (ICICoS). Semarang (Indonesia), 2020, p. 1–6. DOI: 10.1109/ICICoS51170.2020.9299068
9. ASHRAF, E., AREED, N., SALEM, M. H., et al. IoT based intrusion detection systems from the perspective of machine and deep learning: A survey and comparative study. Delta University Scientific Journal, 2022, vol. 5, no. 2, p. 367–386. DOI: 10.21608/dusj.2022.275552
10. KUMAR, S., GUPTA, S., ARORA, S. Research trends in network-based intrusion detection systems: A review. IEEE Access, 2021, vol. 9, p. 157761–157779. DOI: 10.1109/ACCESS.2021.3129775
11. TAHRI, R., BALOUKI, Y., JARRAR, A., et al. Intrusion detection system using machine learning algorithms. ITM Web Conference, 2022, vol. 46, p. 1–4. DOI: 10.1051/itmconf/20224602003
12. JARADAT, A. S., BARHOUSH, M. M., BANI EASA, R. S. Network intrusion detection system: Machine learning approach. Indonesian Journal of Electrical Engineering and Computer Science, 2022, vol. 25, no. 2, p. 1151–1158. DOI: 10.11591/ijeecs.v25.i2.pp1151-1158
13. JMILA, H., KHEDER, M. Adversarial machine learning for network intrusion detection: A comparative study. Computer Networks, 2022, vol. 214, p. 1–14. DOI: 10.1016/j.comnet.2022.109073
14. ADITYA, R., NUHA, H. H., PRABOWO, S. Intrusion detection using support vector machine on internet of things dataset. In IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). Solo (Indonesia), 2022, p. 62–66. DOI: 10.1109/COMNETSAT56033.2022.9994392
15. LE, T.-T.-H., OKTIAN, Y. E., KIM, H. XGBoost for imbalanced multiclass classification-based industrial internet of things intrusion detection systems. Sustainability, 2022, vol. 14, no. 14, p. 1–21. DOI: 10.3390/su14148707
16. SAHEED, Y. K., ABIODUN, A. I., MISRA, S., et al. A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Engineering Journal, 2022, vol. 61, no. 12, p. 1–15. DOI: 10.1016/j.aej.2022.02.063
17. ALEESA, A., ZAIDAN, B., ZAIDAN, A., et al. Review of intrusion detection systems based on deep learning techniques: Coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions. Neural Computing and Applications, 2020, vol. 32, no. 14, p. 9827–9858. DOI: 10.1007/s00521-019-04557-3
18. THAMILARASU, G., CHAWLA, S. Towards deep-learning-driven intrusion detection for the Internet of Things. Sensors, 2019, vol. 19, no. 9, p. 1–19. DOI: 10.3390/s19091977
19. XIAO, Y., XING, C., ZHANG, T., et al. An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access, 2019, vol. 7, p. 42210–42219. DOI: 10.1109/ACCESS.2019.2904620
20. AWAJAN, A. A novel deep learning-based intrusion detection system for IoT networks. Computers, 2023, vol. 12, no. 12, p. 1–17. DOI: 10.3390/computers12020034
21. IKHWAN, S., WIBOWO, A., WARSITO, B. Intrusion detection using deep neural network algorithm on the internet of things. In IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). Solo (Indonesia), 2022, p. 84–87. DOI: 10.1109/COMNETSAT56033.2022.9994499
22. KARATAS, G., DEMIR, O. SAHINGOZ, O. K. Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset. IEEE Access, 2020, vol. 8, p. 32150–32162. DOI: 10.1109/ACCESS.2020.2973219
23. CHAWLA, N. V., BOWYER, K. W., HALL, L. O., et al. SMOTE synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 2002, vol. 16, p. 321–357. DOI: 10.1613/jair.953
24. KUMAR, S. Stop Using SMOTE to Handle All Your Imbalanced Data. 2021. [Online] Cited 2023-01-04. Available at: https://towardsdatascience.com/stop-using-smote-to-handle-allyour-imbalanced-data-34403399d3be
25. ULLAH, I., MAHMOUD, Q. A scheme for generating a dataset for anomalous activity detection in IoT networks. In Proceedings of the 33rd Canadian Conference on Artificial Intelligence. Ottawa (Canada), 2020, p. 508–520. DOI: 10.1007/978-3-030-47358-7_52
26. KRAIEM, M. S., SANCHEZ-HERNANDEZ, F., MORENOGARCIA, M. N. Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties. An approach based on association models. Applied Sciences, 2021, vol. 11, no. 18, p. 1–26. DOI: 10.3390/app11188546
27. ISLAM, R., DEVNATH, M. K., SAMAD, M. D., et al. GGNB: Graph-based Gaussian naive Bayes intrusion detection system for CAN bus. Vehicular Communications, 2022, vol. 33, p. 1–11. DOI: 10.1016/j.vehcom.2021.100442
28. NANTHIYA, D., KEERTHIKA, P., GOPAL, S. B., et al. SVM based DDoS attack detection in IoT using Iot-23 botnet dataset. In Innovations in Power and Advanced Computing Technologies (i-PACT). Kuala Lumpur (Malaysia), 2021, p. 1–7. DOI: 10.1109/i-ACT52855.2021.9696569
29. KURNIABUDI, STIAWAN, D., DARMAWIJOYO, et al. Improvement of attack detection performance on the internet of things with PSO-search and random forest. Journal of Computational Science, 2022, vol. 64, p. 1–13. DOI: 10.1016/j.jocs.2022.101833
30. FAYSAL, J. A., MOSTAFA, S. T., TAMANNA, J. S., et al. XGBRF: A hybrid machine learning approach for IoT intrusion detection. Telecom, 2022, vol. 3, no. 1, p. 52–69. DOI: 10.3390/telecom3010003
31. NASCIMENTO, N., ALENCAR, P., COWAN, D. A lifecycle for engineering IoT neural network-based systems. In IEEE International Conference on Big Data (Big Data). Orlando (FL, USA). 2021, p. 2427–2433. DOI: 10.1109/BigData52589.2021.9671413
32. GYAMFI, E., ANCA, J. Intrusion detection in internet of things systems: A review on design approaches leveraging multi-access edge computing, machine learning, and datasets. Sensors, 2022, vol. 22, no. 10, p. 1–33. DOI: 10.3390/s22103744

Keywords: Machine learning, deep learning, Internet of Things (IoT), intrusion detection, traffic modelling

B. Cseppento, A. Retzler, Z. Kollar [references] [full-text] [DOI: 10.13164/re.2023.0264] [Download Citations]
Optimization of the Crest Factor for Complex-Valued Multisine Signals

Multisine signals are commonly used in the measurement of dynamic systems and wireless channels. For optimal measurements with a high dynamic range, a low Crest Factor (CF) excitation signal is required. In this paper, a modified approach to optimize the crest factor for complex-valued multisine signals is presented. The approach uses a nonlinear optimization method where the real and imaginary parts can also be optimized for low CF. Furthermore, extensions of the real-valued multisine CF optimization methods are presented for complex-valued cases. The proposed methods are validated and compared using simulations. Based on the results it is shown that the novel approach can lead to more optimal signal design and lower CF compared to other techniques for complex-valued multisine signals.

1. BEREZVAI, S., KOSSA, A., BACHRATHY, D., et al. Numerical and experimental investigation of the applicability of pellet impacts for impulse excitation. International Journal of Impact Engineering, 2018, vol. 115, p. 19–31. DOI: 10.1016/j.ijimpeng.2018.01.006
2. YADAV, E. S., INDIRAN, T. PRBS based identification and conditional control for an optimal operation of a pilot plant binary distillation column. In 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO). Manama (Bahrain), 2019, p. 1–5. DOI: 10.1109/ICMSAO.2019.8880422
3. XIA, X. System identification using chirp signals and timevariant filters in the joint time-frequency domain. IEEE Transactions on Signal Processing, 1997, vol. 45, no. 8, p. 2072–2084. DOI: 10.1109/78.611210
4. RETZLER, A., CSEPPENTO, B., SWEVERS, J. et al. Improved crest factor minimization of multisine excitation signals using nonlinear optimization. Automatica, 2022, vol. 146, p. 1–6. DOI: 10.1016/j.automatica.2022.110654
5. KOLLAR, I. Frequency Domain System Identificaton Toolbox for MATLAB. Budapest, 2004–2020. [Online]. Available at: http://home.mit.bme.hu/~kollar/fdident/
6. SHIBASAKI, Y., ASAMI, K., KUWANA, A., et al. Crest factor controlled multi-tone signals for analog/mixed-signal IC testing. In IEEE International Test Conference in Asia (ITC-Asia). Tokyo (Japan), 2019, p. 7–12. DOI: 10.1109/ITC-Asia.2019.00015
7. TANTAU, M., PETERSEN, T., WIELITZKA, M., et al. Constrained design of multisine signals for frequency-domain identification of electric drive trains. IFAC-PapersOnLine, 2020, vol. 3, no. 2, p. 8750–8756. DOI: 10.1016/j.ifacol.2020.12.1369
8. YE, X., JIANG, T., MA, Y., et al. A portable, low-cost and highthroughput electrochemical impedance spectroscopy device for pointof-care biomarker detection. Biosensors and Bioelectronics: X, 2023, vol. 13, p. 1–8. DOI: 10.1016/j.biosx.2022.100301
9. ALTHOFF, H., EBERHARDT, M., GEINITZ, et al. Advances in crest factor minimization for wide-bandwidth multi-sine signals with nonflat amplitude spectra. Computer Sciences & Mathematics Forum, 2022, vol. 2, no. 1, p. 1–10. DOI: 10.3390/IOCA2021-10908
10. DU, X., MENG, J., PENG, J., et al. A two-stage optimization framework for fast lithium-ion battery impedance measurement. IEEE Transactions on Power Electronics, 2023, vol. 38, no. 5, p. 5659–5664. DOI: 10.1109/TPEL.2023.3241072
11. BOROŃ, P., DULIŃSKA, J. M., JASIŃKA, D. Advanced model of spatiotemporal mining-induced kinematic excitation for multiple-support bridges based on the regional seismicity characteristics. Applied Sciences, 2022, vol. 12, no. 14, p. 1–26. DOI: 10.3390/app12147036
12. EISENBEIS, J., TINGULSTAD, M., KERN, N., et al. MIMO communication measurements in small cell scenarios at 28 GHz. IEEE Transactions on Antennas and Propagation, 2021, vol. 69, no. 7, p. 4070–4082. DOI: 10.1109/TApp.2020.3044394
13. CSUKA, B., KOLLAR, Z. Software and hardware solutions for channel estimation based on cyclic golay sequences. Radioengineering, 2016, vol. 25, no. 4, p. 801–807. DOI: 10.13164/re.2016.0801
14. SCHRODER, M. Synthesis of low-peak-factor signals and binary sequences with low autocorrelation. IEEE Transactions on Information Theory, 1970, vol. 16, no. 1, p. 85–89. DOI: 10.1109/TIT.1970.1054411
15. VAN DEN BOS, A. A new method for synthesis of lowpeak-factor signals. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1987, vol. 35, no. 1, p. 120–122. DOI: 10.1109/TASSP.1987.1165028
16. VAN DER OUDERAA, E., SCHOUKENS, J., RENNEBOOG, J. Peak factor minimization using a time-frequency domain swapping algorithm. IEEE Transactions on Instrumentation and Measurement, 1988, vol. 37, no. 1, p. 145–147. DOI: 10.1109/19.2684
17. FRIESE, M. Multitione signals with low crest factor. IEEE Transactions on Communications, 1997, vol. 45, no. 10, p. 1338–1344. DOI: 10.1109/26.634697
18. YANG, Y., ZHANG, F., TAO, K., et al. An improved crest factor minimization algorithm to synthesize multisines with arbitrary spectrum. Physiological Measurement, 2015, vol. 36, no. 5, p. 895–910. DOI: 10.1088/0967-3334/36/5/895
19. GUILLAUME, P., SCHOUKENS, J., PINTELON, R., et al. Crestfactor minimization using nonlinear Chebyshev approximation methods. IEEE Transactions on Instrumentation and Measurement, 1991, vol. 40, no. 6, p. 982–989. DOI: 10.1109/19.119778
20. JANEIRO, F. M., HU, Y., RAMOS, P. M. Peak factor optimization of multiharmonic signals using artificial bee colony algorithm. Measurement, 2020, vol. 150, p. 1–8. DOI: 10.1016/j.measurement.2019.107040
21. TELLADO, J., CIOFFI, J. M. Peak power reduction for multicarrier transmission. In Global Telecommunications Conference. Rio de Janeiro (Brazil), 1998, p. 951–955. DOI: 10.1109/GLOCOM.1999.829941
22. ARMSTRONG, J. Peak-to-average reduction for OFDM by repeated clipping and frequency domain filtering. IET Electronics Letters, 2002, vol. 38, no. 5, p. 246–247. DOI: 10.1049/el:20020175
23. BOYD, S. Multitone signals with low crest factor. IEEE Transactions on Circuits and Systems, 1986, vol. 33, no. 10, p. 1018–1022. DOI: 10.1109/TCS.1986.1085837
24. POPOVIC, M. Synthesis of power efficient multitone signals with flat amplitude spectrum. IEEE Transactions on Communications, 1991, vol. 39, no. 7, p. 1031–1033. DOI: 10.1109/26.87205
25. ANDERSSON, J. A. E., GILLIS, J., HORN, G., et al. CasADi: A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 2019, vol. 11, no. 1, p. 1–36. DOI: 10.1007/s12532-018-0139-4
26. WACHTER, A., BIEGLER, L. On the implementation of an interiorfilter line-search algorithm for large-scale nonlinear programming. Mathematical Programming, 2006, vol. 106, no. 1, p. 25–57. DOI: 10.1007/s10107-004-0559-y

Keywords: Multisine, crest factor, PAPR, optimization, complex signal, channel estimation, OFDM

L. Kirasamuthranon, P. Wardkein, J. Koseeyaporn [references] [full-text] [DOI: 10.13164/re.2023.0273] [Download Citations]
Coding and Coherent Decoding techniques for Continuous Single Slope Cyclic Shift Chirp Signal

Chirp signals are currently widely used in broadband and spread spectrum communications due to their advantageous features, such as immunity to fading noise, low power consumption, consistent long-range transmission, and constant bandwidth. As a result, they are applied at the physical layer of the Internet-of-Things (IoT). This study proposes two techniques for encoding and decoding 4-cyclic shift chirp symbols, based on addition and subtraction operations. The proposed techniques have simple structures that can be easily implemented using analog circuits. The proposed encoding techniques reveal the relationship between cyclic-shift chirp symbols and pulse modulating signals (PWM, PPM, and PAM), which has rarely been discussed in prior research. Moreover, the circuits for encoding and decoding of the proposed techniques are implemented by discrete commercial devices at low frequency (25-35kHz) which is suitable for sonar and communication under water. However, this proposed technique is not limited to only low frequency but can also be used in high-frequency bands. Experimental and simulation results also show good agreement to theoretical analysis.

1. LATHI, B. P. Modern Digital and Analog Communication System. 3rd ed. New York (USA): Oxford University Press, 1998. Ch. 4, Amplitude (linear) modulation, p. 151–250. ISBN: 0-19-511009-9
2. FOROUZAN, B. A. Data Communications and Networking. 4th ed. New York (USA): McGraw-Hill, 2007. Ch. 5, Analog transmission, p. 146–148. ISBN: 978-0-07-296775-3
3. LANCASTER, D. Chirp - A new radar technique. Electronics World, 1965, p. 42–43, 59.
4. WINKLER, M. Chirp signals for communications. IEEE WESCON Convention Record, 1962, vol. 14, no. 2.
5. GOTT, G. F., NEWSOME, J. P. HF data transmission using chirp signals. Proceedings of the Institution of Electrical Engineers, 1971, vol. 118, no. 9, p. 1162–1166. DOI: 10.1049/piee.1971.0210
6. REYNDERS, B., POLLIN, S. Chirp spread spectrum as a modulation technique for long range communication. In 2016 Symposium on Communications and Vehicular Technologies (SCVT). Mons (Belgium), 2016, p. 1–5. DOI: 10.1109/SCVT.2016.7797659
7. ROY, R., LOWENSCHUSS, O. Chirp waveform generation using digital samples. IEEE Transactions on Aerospace and Electronic Systems, 1974, vol. 10, no. 1, p. 10–16. DOI: 10.1109/TAES.1974.307958
8. GONZALEZ, J. E., PARDO, J. M., ASENSIO, A., et al. Digital signal generation for LPM-LPI radars. Electronics Letter, 2003, vol. 39, no. 5, p. 464–465. DOI: 10.1049/el:20030316
9. PUZYREV, P. I., KVACHEV, M. A., EROKHIN, V. V. Frequency shift chirp modulation with additional differential phase shift keying. In 2019 20th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM). Erlagol (Russia), 2019, p. 78–82. DOI: 10.1109/EDM.2019.8823174
10. HANIF, M., NGUYEN, H. H. Frequency-shift chirp spread spectrum communications with index modulation. IEEE Internet of Things Journal, 2021, vol. 8, no. 24, p. 17611–17621. DOI: 10.1109/jiot.2021.3081703
11. GRETINGER, M., SECARA, M., FESTILA, CL., et al. Chirp signal generators for frequency response experiments. In 2014 IEEE International Conference on Automation, Quality and Testing, Robotics. Cluj-Napoca (Romania), 2014, p. 1–4. DOI: 10.1109/AQTR.2014.6857860
12. TELKAMP, T. LoRa, LoRaWAN, and the challenges of long-range networking in shared spectrum. Cognitive Radio Platform NL, 2015.
13. LEE, G., PARK, W., KANG, T., et al. Chirp- based FHSS receiver with recursive symbol synchronization for underwater acoustic communication. Sensors, 2018, vol. 18, no. 12, p. 1–18. DOI: 10.3390/s18124498
14. NAMIAS, V. The fractional order Fourier transform and its application to quantum mechanics. IMA Journal of Applied Mathematics, 1980, vol. 25, no. 3, p. 241–265. DOI: 10.1093/imamat/25.3.241
15. ALMEIDA, L. B. The fractional Fourier transform and timefrequency representations. IEEE Transactions on Signal Processing, 1994, vol. 42, no. 11, p. 3084–3091. DOI: 10.1109/78.330368
16. OZAKTAS, H. M., ARIKAN, O., KUTAY, M. A., et al. Digital computation of the fractional Fourier transform. IEEE Transactions on Signal Processing, 1996, vol. 44, no. 9, p. 2141–2150. DOI: 10.1109/78.536672
17. SFORZA, F. (NANOSCALE LABS). Communications System. US patent US 8.406.275 B2, 2013.
18. HISCOCK, P. D. (CAMBRIDGE SILICON RADIO LIMITED) Chirp Communications. US Patent US 8.718.117 B2, 2014.
19. GOURSAUD, C., GORCE, J. M. Dedicated networks for IoT: PHY/MAC state of the art and challenges. EAI Endorsed Transactions on Internet of Things, 2015, vol. 1, no. 1, p. 1–11. DOI: 10.4108/eai.26-10-2015.150597
20. SPRINGER, A., GUGLER, W., HUEMER, M., et al. Spread spectrum communications using chirp signals. In IEEE/AFCEA EUROCOMM 2000. Information Systems for Enhanced Public Safety and Security. Munich (Germany), 2000, p. 166–170. DOI: 10.1109/EURCOM.2000.874794
21. MROUE, H., NASSER, A., PARREIN, B., et al. Analytical and simulation study for LoRa modulation. In 2018 25th International Conference on Telecommunication (ICT). Saint-Malo (France), 2018, p. 655–659. DOI: 10.1109/ICT.2018.8464879
22. BOYLESTAD, R. L., NASHELSKY, L. Electronic Devices and Circuit Theory. 11th ed. New York (USA): Pearson, 2012. Ch. Diode applications. p. 78–91. ISBN: 978-0132622264
23. KLAUDER, J. R., PRICE, A. C., DARLINGTON, S., et al. The theory and design of chirp radars. Bell System Technical Journal, 1960, vol. 39, no. 4, p. 745–808. DOI: 10.1002/j.1538-7305.1960.tb03942.x
24. ALSHAREF, M. A. Constant-envelope multi-level chirp modulation: Properties, receivers, and performance. Ph.D. Thesis. Dept. Electrical and Computer Eng., Univ. of Western Ontario, (Ontario, Canada), 2016.
25. ALSHAREF, M., HAMED, A., RAO, R. K. Error rate performance of digital chirp communication system over fading channels. In Proceedings of the World Congress on Engineering and Computer Science (WCECS 2015). San Francisco (USA), 2015, [Online] Available at: http://www.iaeng.org/ WCECS2015
26. PROAKIS, J. G., SALEHI, M. Digital Communications. 5th ed. New York (USA): McGraw-Hill, 2008. Ch. 13, Fading channels I: Characterization and signaling, p. 830–898. ISBN: 978–0–07–295716–7
27. KAMINSKY, J., SIMANJUNTAK, L. Chirp slope keying for underwater communications. In Proceedings of SPIE Sensors, and Command, Control, Communications, and Intelligence (C31) Technologies for Homeland Security and Homeland Defense IV Conference. Orlando (FL, USA), 2005, vol. 5778, p. 894–905. DOI: 10.1117/12.605426

Keywords: chirp signal, chirp symbol, cyclic-shift chirp modulation and demodulation, chirp signal spectrum, chirp spread spectrum.

S. Xiao, H. Tao, X. Shen, L. Zhang, M. Hu [references] [full-text] [DOI: 10.13164/re.2023.0287] [Download Citations]
Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio

Multitarget tracking (MTT) for image processing in low signal-to-noise ratio (SNR) is difficult and computationally expensive because the distinction between the target and the background is small. Among the current MTT algorithms, Random Finite Set (RFS) based filters are computationally tractable. However, the probability hypothesis density (PHD) filter, despite its low computational complexity, is not suitable for MTT in low SNR. The generalized labeled multi-Bernoulli (GLMB) filter and its fast implementation are unsuitable for realtime MTT due to their high computational complexity. To achieve realtime MTT in low SNR, a joint PHD filter and Hungarian assignment algorithm is first proposed in this work. The PHD filter is used for preliminary tracking of targets while the Hungarian assignment algorithm is employed to complete the association process. To improve the tracking performance in low SNR, a new track must undergo a trial period and a valid track will be terminated only if it is not detected for several frames. The simulation results show that the proposed MTT algorithm can achieve stable tracking performance in low SNR with small computational complexity. The proposed filter can be applied to MTT in low SNR that require realtime implementation.

1. EBENEZER, S., PAPANDREOU-SUPPAPPOLA, A. Generalized recursive track-before-detect with proposal partitioning for tracking varying number of multiple targets in low SNR. IEEE Transactions on Signal Processing, 2016, vol. 64, no. 11, p. 2819–2834. DOI: 10.1109/TSP.2016.2523455
2. RICHARDS, M. Fundamentals of Radar Signal Processing. 1st ed. New York (USA): McGraw-Hill, 2005. ISBN: 9780070607378
3. RISTIC, B., ARULAMPALAM, S., GORDON, N. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Norwood (USA): Artech House, 2004. ISBN: 9781580536318
4. BARNIV, Y. Dynamic programming solution for detecting dim moving targets. IEEE Transactions on Aerospace and Electronic Systems, 1985, vol. 21, no. 1, p. 144–156. DOI: 10.1109/TAES.1985.310548
5. BO, J., YU, H., WANG, G. The HT-TBD algorithm for large maneuvering targets with fewer beats and more groups. In Proceedings of IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). Chongqing (China), 2021, p. 202–206. DOI: 10.1109/IMCEC51613.2021.9482140
6. ELHOSHY, M., GEBALI, F., GULLIVER, T. Expanding window dynamic-programming-based track-before-detect with order statistics in Weibull distributed clutter. IEEE Transactions on Aerospace and Electronic Systems, 2020, vol. 56, no. 4, p. 2564–2575. DOI: 10.1109/TAES.2019.2948451
7. TONISSEN, S., BAR-SHALOM, Y. Maximum likelihood trackbefore-detect with fluctuating target amplitude. IEEE Transactions on Aerospace and Electronic Systems, 1998, vol. 34, no. 3, p. 796 to 809. DOI: 10.1109/7.705887
8. SU, H., WU, T., LIU, H., et al. Rao-Blackwellised particle filter based track-before-detect algorithm. IET Signal Processing, 2008, vol. 2, no. 2, p. 169–176. DOI: 10.1049/iet-spr:20070075
9. CLARK, D., RISTIC, B., VO, B.-N., et al. Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR. IEEE Transactions on Signal Processing, 2010, vol. 58, no. 1, p. 26–37. DOI: 10.1109/TSP.2009.2030640
10. YANG, B., WANG, J., YUAN, C., et al. Multi-object Bayesian filters with amplitude information in clutter background. Signal Processing, 2018, vol. 152, p. 22–34. DOI: 10.1016/j.sigpro.2018.05.004
11. DU, R., LIU, L., BAI, X., et al. A new scatterer trajectory association method for ISAR image sequence utilizing multiple hypothesis tracking algorithm. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–13. DOI: 10.1109/TGRS.2021.3087192
12. HE, S., SHIN, H., TSOURDOS, A. Distributed multiple model joint probabilistic data association with Gibbs sampling-aided implementation. Information Fusion, 2020, vol. 64, p. 20–31. DOI: 10.1016/j.inffus.2020.04.007
13. MAHLER, R. Statistical Multisource Multitarget Information Fusion. Norwood (USA): Artech House, 2007. ISBN: 978-1596930926
14. MAHLER, R. Advances in Statistical Multisource-Multitarget Information Fusion. Norwood (USA): Artech House, 2014. ISBN: 9781608077984
15. MAHLER, R. Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, 2003, vol. 39, no. 4, p. 1152–1178. DOI: 10.1109/TAES.2003.1261119
16. VO, B.-N., SINGH, S., DOUCET, A. Sequential Monte Carlo methods for multi-target filtering with random finite sets. IEEE Transactions on Aerospace and Electronic Systems, 2005, vol. 41, no. 4, p. 1224–1245. DOI: 10.1109/TAES.2005.1561884
17. VO, B.-N., MA, W.-K. The Gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing, 2006, vol. 54, no. 11, p. 4091–4104. DOI: 10.1109/TSP.2006.881190
18. PANTA, K., VO, B.-N., SINGH, S. Novel data association schemes for the probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 2007, vol. 43, no. 2, p. 556–570. DOI: 10.1109/TAES.2007.4285353
19. XIAO, H., LI, Y., FU, Q. Identification and tracking of towed decoy and aircraft using multiple-model improved labeled P-PHD filter. Digital Signal Processing, 2015, vol. 46, p. 49–58. DOI: 10.1016/j.dsp.2015.07.005
20. WANG, S., BAO, Q., CHEN, Z. Refined PHD filter for multi-target tracking under low detection probability. Sensors, 2019, vol. 19, no. 13, p. 1–17. DOI: 10.3390/s19132842
21. VO, B.-T., VO, B.-N. Labeled random finite sets and multi-object conjugate priors. IEEE Transactions on Signal Processing, 2013, vol. 61, no. 13, p. 3460–3475. DOI: 10.1109/TSP.2013.2259822
22. REUTER, S., VO, B.-T., VO, B.-N., et al. The labeled multiBernoulli filter. IEEE Transactions on Signal Processing, 2014, vol. 62, no. 12, p. 3246–3260. DOI: 10.1109/TSP.2014.2323064
23. VO, B.-N., VO, B.-T., PHUNG, D. Labeled random finite sets and the Bayes multi-target tracking filter. IEEE Transactions on Signal Processing, 2014, vol. 62, no. 24, p. 6554–6567. DOI: 10.1109/TSP.2014.2364014
24. KROPFREITER, T., MEYER, F., HLAWATSCH, F. A fast labeled multi-Bernoulli filter using belief propagation. IEEE Transactions on Aerospace and Electronic Systems, 2020, vol. 56, no. 3, p. 2478 to 2488. DOI: 10.1109/TAES.2019.2941104
25. VO, B.-N., VO, B.-T., HOANG, H. An efficient implementation of the generalized labeled multi-Bernoulli filter. IEEE Transactions on Signal Processing, 2017, vol. 65, no. 8, p. 1975–1987. DOI: 10.1109/TSP.2016.2641392
26. BEWLEY, A., GE, Z., OTT, L., et al. Simple online and realtime tracking. In Proceedings of 2016 IEEE International Conference on Image Processing (ICIP). Phoenix (USA), 2016, p. 3464–3468. DOI: 10.1109/ICIP.2016.7533003
27. KUHN, H. The Hungarian method for the assignment problem. Naval Research Logistics, 1955, vol. 2, p. 83–97. DOI: 10.1007/978-3-540-68279-0_2
28. WOJKE, N., BEWLEY, A., PAULUS, D. Simple online and realtime tracking with a deep association metric. In Proceedings of 2017 IEEE International Conference on Image Processing (ICIP). Beijing (China), 2017, p. 3645–3649. DOI: 10.1109/ICIP.2017.8296962
29. LI, X. R., JILKOV, V. P. Survey of maneuvering target tracking: Part I. Dynamic models. IEEE Transactions on Aerospace and Electronic Systems, 2003, vol. 39, no. 4, p. 1333–1364. DOI: 10.1109/TAES.2003.1261132
30. SCHUHMACHER, D., VO, B.-T., VO, B.-N. A consistent metric for performance evaluation of multi-object filters. IEEE Transactions on Signal Processing, 2008, vol. 56, no. 8, p. 3447–3457. DOI: 10.1109/TSP.2008.920469
31. BEARD, M., VO, B.-T., VO, B.-N. OSPA(2): Using the OSPA metric to evaluate multi-target tracking performance. In Proceedings of 2017 International Conference on Control, Automation and Information Sciences (ICCAIS). Chiang Mai (Thailand), 2014, p. 86–91. DOI: 10.1109/ICCAIS.2017.8217598

Keywords: Hungarian assignment algorithm, PHD filter, multitarget tracking (MTT), low signal-to-noise ratio (SNR)