S. Rana, S. Pramanik, D. Mitra, C. Koley [references] [full-text] [DOI: 10.13164/re.2025.0575] [Download Citations]
Design and Analysis of Multifunctional Metasurface for Linear and Circular Polarized Incident Wave

A passive multifunctional metasurface design has been proposed in this work for both linear and circular polarized incidences, between a frequency range of 4-7 GHz. The proposed unit cell structure comprises of concentric split pentagonal rings, loaded with two lumped capacitors on the top layer, having single dielectric layer. The main functionalities include polarization selective absorption (4.31-4.36 GHz) and reflective cross-polarization conversion (5.79-6.02 GHz) for linear polarized incidences. Moreover, polarization handedness maintaining reflection (4.57-5.45 GHz) and polarization selective absorption (6.12-6.26 GHz) for circular polarized incidences has been achieved. A sample prototype of 10 X 10-unit cells (2lambda_0 X 2lambda_0), was fabricated and the experimental results were verified with the simulated ones. Since the proposed design has such diversified functionalities for both linear and circular polarized incidences, it can find probable applications in polarimetric imaging techniques, polarization modulation devices, polarization sensors etc.

1. HOLLOWAY, C., KUESTER, E., GORDON, J., et al. An overview of the theory and applications of metasurfaces: The two-dimensional equivalents of metamaterials. IEEE Antennas and Propagation Magazine, 2012, vol. 54, no. 2, p. 10–35. DOI: 10.1109/MAP.2012.6230714
2. PENDRY, J. A chiral route to negative refraction. Science, 2004, vol. 306, no. 5700, p. 1353–1354. DOI: 10.1126/science.1104467
3. MA, H., WANG, G., KONG, G., et al. Independent controls of differently-polarized reflected waves by anisotropic metasurfaces. Scientific Reports, 2015, vol. 5, p. 1–6. DOI: 10.1038/srep09605
4. MODI, A., BALANIS, C., BIRTCHER, C., et al. Novel design of ultrabroadband radar cross section reduction surfaces using artificial magnetic conductors. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 10, p. 5406–5417. DOI: 10.1109/TAP.2017.2734069
5. ZHANG, C., CHENG, Q., YANG, J., et al. Broadband metamaterial for optical transparency and microwave absorption. Applied Physics Letters, 2017, vol. 110, no. 14, p. 1–5. DOI: 10.1063/1.4979543
6. CHENG, Y., LUO, H., CHEN, F., et al. Broadband metamaterial microwave absorber based on asymmetric sectional resonator structures. Applied Physics Letters, 2020, vol. 127, no. 21, p. 1–7. DOI: 10.1063/5.0002931
7. PANG, Y., SHEN, Y., LI, Y., et al. Water-based metamaterial absorbers for optical transparency and broadband microwave absorption. Applied Physics Letters, 2018, vol. 123, no. 15, p. 1–5. DOI: 10.1063/1.5023778
8. SONG, S., MA, X., PU, M., et al. Tunable multiband polarization conversion and manipulation in vanadium dioxide-based asymmetric chiral metamaterial. Applied Physics Express, 2018, vol. 11, no. 4, p. 1–4. DOI: 10.7567/APEX.11.042004
9. DONG, G., QIN, C., LV, T., et al. Dynamic chiroptical responses in transmissive metamaterial using phase-change material. Journal of Physics D: Applied Physics, 2020, vol. 53, no. 28, p. 1–9. DOI: 10.1088/1361-6463/ab8516
10. ZHU, B., FENG, Y., ZHAO, J., et al. Polarization modulation by tunable electromagnetic metamaterial reflector/absorber. Optics Express, 2010, vol. 18, no. 22, p. 23196–23202. DOI: 10.1364/OE.18.023196
11. BASKEY, H. B., JOHARI, E., AKHTAR, M. J., et al. Metamaterial structure integrated with a dielectric absorber for wideband reduction of antennas radar cross section. IEEE Transactions on Electromagnetic Compatibility, 2017, vol. 59, no. 4, p. 1–10. DOI: 10.1109/TEMC.2016.2639060
12. SHARMA, S. K., GHOSH, S., SRIVASTAVA, K. V., et al. An ultrathin triple-band polarization-insensitive metamaterial absorber for S, C and X band applications. Applied Physics A, 2016, vol. 122, p. 1–8. DOI: 10.1007/s00339-016-0588-4
13. SINGH, A. K., ABEGAONKAR, M. P., KOUL, S. K., et al. A triple band polarization insensitive ultrathin metamaterial absorber for S-, C-, and X-bands. Progress In Electromagnetics Research M, 2019, vol. 77, p. 187–197. DOI: 10.2528/PIERM19011802
14. PAQUAY, M., IRIARTE, J. C., EDERRA, I., et al. Thin AMC structure for radar cross-section reduction. IEEE Transactions on Antennas and Propagation, 2007, vol. 55, no. 12, p. 1–9. DOI: 10.1109/TAP.2007.910306
15. ZUO, P., LI, T., WANG, M., et al. Miniaturized polarization insensitive metamaterial absorber applied on EMI suppression. IEEE Access, 2019, vol. 8, p. 6583–6590. DOI: 10.1109/ACCESS.2019.2957308
16. STOJANOVIC, D. B., GLIGORIC, G., BELICEV, P. P., et al. Circular polarization selective metamaterial absorber in terahertz frequency range. IEEE Journal of Selected Topics in Quantum Electronics, 2021, vol. 27, no. 1, p. 1–6. DOI: 10.1109/JSTQE.2020.3024570
17. PAN, M., LI, Q., HONG, Y., et al. Circular-polarization-sensitive absorption in refractory metamaterials composed of molybdenum zigzag arrays. Optics Express, 2018, vol. 26, no. 14, p. 17772, p. 1–9. DOI: 10.1364/OE.26.017772
18. JING, L., WANG, Z., YANG, Y., et al. Chiral metamirrors for broadband spin-selective absorption. Applied Physics Letters, 2017, vol. 110, no. 23, p. 1–7. DOI: 10.1063/1.4985132
19. TANG, B., LI, Z., PALACIOS, E., et al. Chiral-selective plasmonic metasurface absorbers operating at visible frequencies. IEEE Photonics Technology Letters, 2017, vol. 29, no. 3, p. 295–298. DOI: 10.1109/LPT.2016.2647262
20. FU, C., SUN, Z., HAN, L., et al. Dual-bandwidth linear polarization converter based on anisotropic metasurface. IEEE Photonics Journal, 2020, vol. 12, no. 2, p. 1–11. DOI: 10.1109/JPHOT.2019.2962336
21. IBRAHIM, M. S., MAHMOUD, A., AWAMRY, A., et al. Wideband anisotropic unit cell design for perfect cross-polarization conversion. IEEE International Symposium on Antennas and Propagation USNCURSI Radio Science Meeting. Atlanta (GA, USA), 2019, p. 1–2. DOI: 10.1109/APUSNCURSINRSM.2019.8889282
22. AKO, R. T., LEE, W. S. L., BHASKARAN, M., et al. Broadband and wide-angle reflective linear polarization converter for terahertz waves. APL Photonics, 2019, vol. 4, no. 9, p. 96104–96110.DOI: 10.1063/1.5116149
23. HUANG, X., CHEN, J., YANG, H., et al. High-efficiency wideband reflection polarization conversion metasurface for circularly polarized waves. Journal of Applied Physics, 2017, vol. 122, no. 4, p. 1–4. DOI: 10.1063/1.4996643
24. YANG, D., LIN, H., HUANG, X., et al. Dual broadband metamaterial polarization converter in microwave regime. Progress In Electromagnetics Research Letters, 2016, vol. 61, p. 71–76. DOI: 10.2528/PIERL16033004
25. LIU, Y., HAO, Y., LI, K., et al. Radar cross section reduction of a microstrip antenna based on polarization conversion metamaterial. IEEE Antennas and Wireless Propagation Letters, 2015, vol. 15, p. 80–83. DOI: 10.1109/LAWP.2015.2430363
26. BAKSHI, S. C., MITRA, D., GHOSH, S., et al. A frequency selective surface based reconfigurable rasorber with switchable transmission/reflection band. IEEE Antennas and Wireless Propagation Letters, 2021, vol. 18, no. 1, p. 29–33. DOI: 10.1109/LAWP.2018.2878858
27. LIN, B., GUO, J., CHU, P., et al. Multiple-band linear-polarization conversion and circular polarization in reflection mode using a symmetric anisotropic metasurface. Physical Review Applied, 2018, vol. 9, no. 2, p. 1–10. DOI: 10.1103/PhysRevApplied.9.024038
28. LI, Y., LI, H., WANG, Y., et al. A novel switchable absorber/linear converter based on active metasurface and its application. IEEE Transactions on Antennas and Propagation, 2020, vol. 68, no. 11, p. 7688–7693. DOI: 10.1109/TAP.2020.2980301
29. WANG, J., YANG, R., MA, R., et al. Reconfigurable multifunctional metasurface for broadband polarization conversion and perfect absorption. IEEE Access, 2020, vol. 8, p. 105815–105823. DOI: 10.1109/ACCESS.2020.3000042
30. DUTTA, R., MITRA, D., GHOSH, J., et al. Dual-band multifunctional metasurface for absorption and polarization conversion. International Journal of RF and Microwave Computer-Aided Engineering, 2020, vol. 30, no. 7, p. 1–7. DOI: 10.1002/mmce.22200
31. HA, D. T., DZUNG, D. N., NGOC, N. V., et al. Switching between perfect absorption and polarization conversion, based on hybrid metamaterial in the GHz and THz bands. Journal of Physics D: Applied Physics, 2021, vol. 54, no. 23, p. 1–11. DOI: 10.1088/1361-6463/abeb97
32. PITILAKIS, A., TSILIPAKOS, O., LIU, F., et al. A multi-functional reconfigurable metasurface: Electromagnetic design accounting for fabrication aspects. IEEE Transactions on Antennas and Propagation, 2021, vol. 69, no. 3, p. 1440–1454. DOI: 10.1109/TAP.2020.3016479
33. SUN, S., JIANG, W., GONG, S., et al. Reconfigurable linear-to-linear polarization conversion metasurface based on PIN diodes. IEEE Antennas and Wireless Propagation Letters, 2018, vol. 17, no. 9, p. 1722–1726. DOI: 10.1109/LAWP.2018.2864797
34. YANG, X., WEN, E., BHARADIA, D., et al. Multifunctional metasurface: Simultaneous beam steering, polarization conversion, and phase offset. IEEE Transactions on Antennas and Propagation, 2024, vol. 72, no. 5, p. 4589–4593. DOI: 10.1109/TAP.2024.3371697
35. POUYANFAR, N., NOURINIA, J., GHOBADI, C., et al. Multiband and multifunctional polarization converter using an asymmetric metasurface. Scientific Reports, 2021, vol. 11, p. 1–15. DOI: 10.1038/s41598-021-88771-x
36. LI, L., LI, Y., WU, Z., et al. Novel polarization-reconfigurable converter based on multilayer frequency-selective surfaces. Proceedings of the IEEE, 2015, vol. 103, no. 7, p. 1057–1070. DOI: 10.1109/JPROC.2015.2437611
37. KARAMIRAD, M., GHOBADI, C., NOURINIA, J., et al. Metasurfaces for wideband and efficient polarization rotation. IEEE Transactions on Antennas and Propagation, 2021, vol. 69, no. 3, p. 1799–1804. DOI: 10.1109/TAP.2020.3012828
38. CHENG, H., WEI, X., YU, P., et al. Integrating polarization conversion and nearly perfect absorption with multifunctional metasurfaces. Applied Physics Letters, 2017, vol. 110, p. 171903–171907. DOI: 10.1063/1.4982240
39. GHOSH, S., GHOSH, J., SANTOSHKUMAR SINGH, M., et al. A low-profile multifunctional metasurface reflector for multiband polarization transformation. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, vol. 70, no. 1, p. 76–80. DOI: 10.1109/TCSII.2022.3202085
40. SHIRZAD, H., GHOBADI, C., NOURINIA, J., et al. Single layer multi-band transmissive type linear to circular polarization converter with wide angular stability for C-, X-, and Ku band applications. IEEE Access, 2024, vol. 12, p. 14083–14093. DOI: 10.1109/ACCESS.2024.3355197
41. DUTTA, R., GHOSH, J., YANG, Z., et al. Multi-band multifunctional metasurface-based reflective polarization converter for linear and circular polarizations. IEEE Access, 2021, vol. 9, p. 152738–152748. DOI: 10.1109/ACCESS.2021.3128190
42. KAYA, Y., HASAR, U. C., OZTURK, H., et al. Multi-functional, multi-band, low-profile, ultra-thin, and cost-effective metasurface based polarization converter. Measurement, 2025, vol. 248, p. 1–13. DOI: 10.1016/j.measurement.2025.116894
43. LI, Z., YANG, R., WANG, J., et al. Multifunctional metasurface for broadband absorption, linear and circular polarization conversions. Optical Materials Express, 2021, vol. 11, no. 10, p. 3507–3517. DOI: 10.1364/OME.437474
44. SINGH, V., BHATTACHARYYA, S., AGRAHARI, R., et al. A low profile tri-functional metasurface toward polarization conversions and absorption. IEEE Antennas and Wireless Propagation Letters, 2024, vol. 23, no. 9, p. 2593–2597. DOI: 10.1109/LAWP.2024.3400375
45. PENG, L., LI, X., JIANG, X., et al. A novel THz half-wave polarization converter for cross-polarization conversions of both linear and circular polarizations and polarization conversion ratio regulating by graphene. IEEE Journal of Lightwave Technology, 2018, vol. 36, no. 19, p. 4250–4258. DOI: 10.1109/JLT.2018.2836904

Keywords: Lumped capacitor, multifunctional metasurface, polarization converter, polarization selective absorber

L. Leitold, M. Alrwashdeh, Z. Kollar [references] [full-text] [DOI: 10.13164/re.2025.0583] [Download Citations]
High-Performance Multi-Precision Tool for Floating-Point Computations

This paper presents a MATLAB toolbox for multiple-precision arithmetic, enabling high-precision numerical computations beyond the standard double-precision format. The development step and functionality of the toolbox are described, and how it integrates seamlessly with the MATLAB computational ecosystem, offering a user-friendly interface. The performance enhancement and usability of the toolbox are demonstrated through four use cases where floating-point arithmetic becomes an issue. These benchmark results illustrate the toolbox's computational accuracy and performance, highlighting its ability to mitigate numerical instabilities and roundoff errors in sensitive computations. The toolbox empowers researchers and engineers to implement more reliable models, test advanced algorithms, and validate system designs and a diverse set of operations for applications that require enhanced precision, such as numerical analysis, scientific computing, and applied mathematics.

1. WIDROW, B., KOLLAR, I. Quantization Noise: Roundoff Error in Digital Computation, Signal processing, Control, and Communications. 1st ed. Cambridge (UK): Cambridge University Press, 2008. ISBN: 9780511754661
2. JANICKI, M., NAPIERALSKI, A. Considerations on electronic system compact thermal models in the form of RC ladders. In Proceedings of the IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM). Polyana (Ukraine). 2019, p. 1–4. DOI: 10.1109/CADSM.2019.8779274
3. AL-RIKABI, H., RENCZES, B. Floating-point quantization analysis of multi-layer perceptron artificial neural networks. Journal of Signal Processing Systems, 2024, vol. 96, no. 4–5, p. 301–312. DOI: 10.1007/s11265-024-01911-0
4. FLINT: Fast Library for Number Theory. [Online] Cited 2025-09-19. Available at: https://flintlib.org/
5. HIDA, Y., LI, X. S., BAILEY, D. H. Library for Double-Double and Quad-Double Arithmetic. [Online] Cited 2008-05-08, p. 1–24. Available at: https://www.davidhbailey.com/dhbpapers/qd.pdf
6. AHLSTROM, Å. High-Performance Computing with Quad-Precision on Two GPUs. Akademi University, Master’s Thesis, 2024. [Online] Cited 2025-09-19. Available at: https://www.doria.fi/bitstream/handle/10024/190456/ahlstrom_anders.pdf
7. FREE SOFTWARE FOUNDATION. The GNU Multiple Precision Arithmetic Library. [Online] Cited 2025-04-14. Available at https://gmplib.org/
8. FOUSSE, L., HANROT, G., LEFEVRE, V., et al. MPFR: A multiple precision binary floating-point library with correct rounding. ACM Transactions on Mathematical Software, 2007, vol. 33, no. 2, p. 1–13. DOI: 10.1145/1236463.1236468
9. THE MATHWORKS, INC. Symbolic Math Toolbox MATLAB Help Center. [Online] Cited 2025-04-14. Available at: https://www.mathworks.com/help/symbolic/index.html
10. ADVANPIX LLC. Multiprecision Computing Toolbox for MATLAB. [Online] Cited 2025-04-14. Available at: https://www.advanpix.com/
11. BARROWES, B. Multiple Precision Toolbox for MATLAB v2.0. [Online] Cited 2025-04-14. Available at: https://www.mathworks.com/matlabcentral/fileexchange/6446-multiple-precision-toolbox-for-matlab
12. LEITOLD, L., CSEPPENTO, B., KOLLAR, Z. MP Toolbox. [Online] Cited 2025-04-14. Available at: https://github.com/LLeitold/MP-Toolbox
13. LEITOLD, L., CSEPPENTO, B., KOLLAR, Z. Development of an efficient and fast computational tool for multi-precision floating point numbers. In Proceedings of the 35th International Conference Radioelektronika (RADIOELEKTRONIKA). Brno (Czech Republic). 2025, p. 1–5. DOI: 10.1109/RADIOELEKTRONIKA65656.2025.11008412
14. ANSI/IEEE Std 754-1985. IEEE Standard for Binary Floating-Point Arithmetic. 1985, p. 1–20. DOI: 10.1109/IEEESTD.1985.82928
15. IEEE Std 754-2008. IEEE Standard for Floating-Point Arithmetic. 2008, p. 1–70. DOI: 10.1109/IEEESTD.2008.4610935
16. IEEE Std 754-2019 (Revision of IEEE 754-2008). IEEE Standard for Floating-Point Arithmetic. 2019, p. 1–84. DOI: 10.1109/IEEESTD.2019.8766229
17. WANG, S., KANWAR, P. BFloat16: The Secret to High Performance on Cloud TPUs. [Online] Cited 2025-04-14. Available at: https://cloud.google.com/blog/products/ai-machine-learning/bfloat16-the-secret-to-high-performance-on-cloud-tpus
18. NAKATA, M. MPLAPACK version 2.0.1 (User Manual). 136 pages. [Online] Cited 2025-04-14. Available at: https://arxiv.org/pdf/2109.13406. DOI: 10.48550/arXiv.2109.13406
19. TODD, J. Computational problems concerning the Hilbert matrix. Journal of Research of the National Bureau of Standards, 1961, vol. 65, no. 1, p. 19–22. DOI: 10.6028/jres.065B.005
20. NIHARIKA, R., IMTIYAZ AHMED, B., NIRANJAN, L., et al. Custom precision method of floating-point operations of FFT processing for optimized area and delay performance. In Proceedings of the International Conference on Intelligent Innovations in Engineering and Technology (ICIIET). Coimbatore (India), 2022, p. 171–177. DOI: 10.1109/ICIIET55458.2022.9967519
21. ZHAO, X., YAN, C., WANG, C., et al. Design of approximate floating-point FFT with mantissa bit-width adjustment algorithm. In Proceedings of the IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). Shenzhen (China), 2022, p. 265–269. DOI: 10.1109/APCCAS55924.2022.10090295
22. TEYMOURZADEH, R., ABIGO, M., HONG, M. Characteristic analysis of 1024-point quantized Radix-2 FFT/IFFT processor. In Proceedings of the 10th IEEE International Conference on Semiconductor Electronics (ICSE). Kuala Lumpur (Malaysia), 2012, p. 664–668. DOI: 10.1109/SMElec.2012.6417231
23. WING, O. Classical Circuit Theory. 1st ed. New York (USA): Springer, 2008. DOI: 10.1007/978-0-387-09740-4
24. CODECASA, L. Canonical forms of one-port passive distributed thermal networks. IEEE Transactions on Components and Packaging Technologies, 2005, vol. 28, no. 1, p. 5–13. DOI: 10.1109/TCAPT.2004.843182

Keywords: MATLAB, floating-point, GMP, MPFR, multiple-precision, quantization, QNP

R. P. K. Emani, P. Telagathoti, P. Nizampatanam [references] [full-text] [DOI: 10.13164/re.2025.0591] [Download Citations]
Fusion Based Method for Wide Band Speech Reconstruction Over Legacy Telephone Networks

Public Switched Telephone Networks (PSTNs) are limited to Restricted Band (RB) speech in the 0-4 kHz range, which reduces speech quality compared to Wideband (WB) speech (0-8 kHz). This study proposes a transform-based hybrid steganography technique combining Curvelet Transform (CT) and Fast Fourier Transform (FFT) to enhance RB speech quality while maintaining full PSTNs compatibility. The Curvelet Transform, capable of representing directional and edge-like features across multiple scales and angles, allows efficient capture of speech components such as formant transitions and unvoiced consonants, which are critical for improving quality and intelligibility. In the proposed approach, WB speech is decomposed into RB and Extended Band (4-8 kHz) components. Detailed coefficients of the RB are extracted using CT, and spread parameters of the Extended Band are embedded within the RB signal. Inverse CT and FFT are performed to form a Composite Restricted Band (CRB) signal for transmission. At the receiver, the embedded parameters are recovered to reconstruct the Extended Band, which is merged with the CRB signal to yield a high-quality Reconstructed Wideband (RWB) signal. Tests with participants aged 60-75 years demonstrated superior performance over conventional methods, with strong robustness to channel and quantization noise.

1. JAX, P., VARY, P. Bandwidth extension of speech signals: A catalyst for the introduction of wideband speech coding? IEEE Communications Magazine, 2006, vol. 44, no. 5, p. 106–111. DOI: 10.1109/MCOM.2006.1637954
2. GAJJAR, P., BHATT, N., KOSTA, Y. Artificial bandwidth extension of speech and its applications in wireless communication systems: A review. In Proceedings of the 2012 International Conference on Communication Systems and Network Technologies (CSNT). Rajkot (India), 2012, p. 563–568. DOI: 10.1109/CSNT.2012.127
3. QIAN, Y., KABAL, P. Dual-mode wideband speech recovery from narrowband speech. In Proceedings of European Conference on Speech Communication and Technology. Geneva (Switzerland), 2003, p. 1433–1436. DOI: 10.21437/EUROSPEECH.2003-250
4. VASEGHI, S. S., ZAVAREHEI, E., YAN, Q., et al. Speech bandwidth extension: Extrapolations of spectral envelop and harmonicity quality of excitation. In IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings. Toulouse(France), 2006, p. 1–4. DOI: 10.1109/ICASSP.2006.1660786
5. EPPS, J., HOLMES, W. H. A new technique for wideband enhancement of coded narrowband speech. In IEEE Workshop on Speech Coding Proceedings: Model, Coders, and Error Criteria (Cat. No.99EX351). Porvoo (Finland), 1999, p. 174–176. DOI: 10.1109/SCFT.1999.781522
6. HU, R., KRISHNAN, V., ANDERSON, D. V. Speech bandwidth extension by improved codebook mapping towards increased phonetic classification. In Proceedings of Interspeech. Lisbon (Portugal), 2005, p. 1501–1504. DOI: 10.21437/INTERSPEECH.2005-527
7. NAKATOH, Y., TSUSHIMA, M., NORIMATSU, T. Generation of broadband speech from narrowband speech using piecewise linear mapping. In Proceedings of the 5th European Conference on Speech Communication and Technology (EUROSPEECH). Rhodes (Greece), 1997, p. 1643–1646. DOI: 10.21437/EUROSPEECH.1997-469
8. PULAKKA, H., REMES, U., PALOMAKI, K., et al. Speech bandwidth extension using Gaussian mixture model-based estimation of the high band mel spectrum. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague (Czech Republic), 2011, p. 5100–5103. DOI: 10.1109/ICASSP.2011.5947504
9. BAUER, P., FINGSCHEIDT, T. An HMM-based artificial bandwidth extension evaluated by cross-language training and test. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Las Vegas (NV, USA), 2008, p. 4589–4592. DOI: 10.1109/ICASSP.2008.4518678
10. PULAKKA, H., ALKU, P. Bandwidth extension of telephone speech using a neural network and a filter bank implementation for high band mel spectrum. IEEE Transactions on Audio, Speech, and Language Processing, 2011, vol. 19, no. 7, p. 2170–2183. DOI: 10.1109/TASL.2011.2118206
11. LEE, B.-K., NOH, K., CHANG, J.-H., et al. Sequential deep neural networks ensemble for speech bandwidth extension. IEEE Access, 2018,vol.6,p.27039–27047.DOI: 10.1109/ACCESS.2018.2833890
12. CHEN, X., YANG, J. Speech bandwidth extension based on Wasserstein generative adversarial network. In Proceedings of the IEEE 21st International Conference on Communication Technology (ICCT). Tianjin (China), 2021, p. 1356–1362. DOI: 10.1109/ICCT52962.2021.9658055
13. LIN, H., YANG, J., CHEN, X. Multi-scale generative adversarial network for speech bandwidth extension. In IEEE 22nd International Conference on Communication Technology (ICCT). Nanjing (China), 2022, p. 1189–1193. DOI: 10.1109/ICCT56141.2022.10073331
14. LI, Y., TAGLIASACCHI, M., RYBAKOV, O., et al. Realtime speech frequency bandwidth extension. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toronto (Canada), 2021, p. 691–695. DOI: 10.1109/ICASSP39728.2021.9413439
15. GUPTA, D., SHEKHAWAT, H.S. Artificial bandwidth extension using H∞ optimization, deep neural network, and speech production model. In Proceedings of the IEEE International Conference on Signal Processing and Communications (SPCOM). Bangalore (India), 2022, p. 1–5. DOI: 10.1109/SPCOM55316.2022.9840805
16. TAHER, T., MAMUN, N., HOSSAIN, M. A. A joint bandwidth expansion and speech enhancement approach using deep neural network. In International Conference on Electrical, Computer and Communication Engineering (ECCE). Chittagong (Bangladesh), 2023, p. 1–4. DOI: 10.1109/ECCE57851.2023.10101546
17. XU, C.-D., LING, X.-P., YING, D.-W. Codec network for speech bandwidth extension. In IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). Nanchang (China), 2021, p. 387–391. DOI: 10.1109/ICBAIE52039.2021.9389968
18. RU, J., JIA, M., ZHAO, Y., et al. A dual-branch neural network for phase-aware speech bandwidth extension. In 6th International Conference on Information Communication and Signal Processing (ICICSP). Xi’an (China), 2023, p. 634–638. DOI: 10.1109/ICICSP59554.2023.10390706
19. SCHMIDT, K., EDLER, B. Blind bandwidth extension of speech based on LPCNet. In 28th European Signal Processing Conference (EUSIPCO). Amsterdam (Netherlands), 2021, p. 426–430. DOI: 10.23919/EUSIPCO47968.2020.9287465
20. NIDADAVOLU, P.S., IGLESIAS, V., VILLALBA, J., et al. Investigation on neural bandwidth extension of telephone speech for improved speaker recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton (UK), 2019, p. 6111–6115. DOI: 10.1109/ICASSP.2019.8682992
21. JAX, P., VARY, P. An upper bound on the quality of artificial bandwidth extension of narrowband speech signals. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Orlando (FL, USA), 2002, p. 237–240. DOI: 10.1109/ICASSP.2002.5743698
22. CHEN, S., LEUNG, H. Artificial bandwidth extension of telephony speech by data hiding. In IEEE International Symposium on Circuits and Systems (ISCAS). Kobe (Japan), 2005, vol. 4, p. 3151–3154. DOI: 10.1109/ISCAS.2005.1465296
23. GAJJAR, P., BHATT, N., KOSTA, Y. Artificial bandwidth extension of speech and its applications in wireless communication systems: A review. In Proceedings of the International Conference on Communication Systems and Network Technology (CSNT). Rajkot (India), 2012, p. 563–568. DOI: 10.1109/CSNT.2012.127
24. CHEN, S., LEUNG, H. Speech bandwidth extension by data hiding and phonetic classification. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Honolulu (HI, USA), 2007, p. 593–596. DOI: 10.1109/ICASSP.2007.366982
25. CHEN, S.S., LEUNG, H., DING, H. Telephony speech enhancement by data hiding. IEEE Transactions on Instrumentation and Measurement, 2007, vol.56, no.1, p.63–74.DOI: 10.1109/TIM.2006.887409
26. CHEN, Z., ZHAO, C., GENG, G., et al. An audio watermark-based speech bandwidth extension method. EURASIP Journal on Audio, Speech, and Music Processing, 2013, vol. 2013, no. 10, p. 1–10. DOI: 10.1186/1687-4722-2013-10
27. VARY, P., GEISER, B. Steganographic wideband telephony using narrowband speech codecs. In Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers (ACSSC). Pacific Grove (CA, USA), 2007, p. 1475–1479. DOI: 10.1109/ACSSC.2007.4487475
28. GEISER, B., VARY, P. Backwards compatible wideband telephony in mobile networks: CELP watermarking and bandwidth extension. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Honolulu (HI, USA), 2007, p. 533–536. DOI: 10.1109/ICASSP.2007.366967
29. GEISER, B., VARY, P. Speech bandwidth extension based on in-band transmission of higher frequencies. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Vancouver (BC, Canada), 2013, p. 7507–7511. DOI: 10.1109/ICASSP.2013.6639122
30. BHATT, N., KOSTA, Y. A novel approach for artificial bandwidth extension of speech signals by LPC technique over proposed GSMFR NB coder using high band feature extraction and various extension of excitation methods. International Journal of Speech Technology, 2015, vol. 18, p. 57–64. DOI: 10.1007/s10772-014-9249-1
31. BHATT, N. S. Simulation and overall comparative evaluation of performance between different techniques for high band feature extraction based on artificial bandwidth extension of speech over proposed global system for mobile full rate narrow band coder. International Journal of Speech Technology, 2016, vol. 19, p. 881–893. DOI: 10.1007/s10772-016-9378-9
32. BHATT, N. S. Implementation and overall performance evaluation of CELP based GSM AMR NB coder over ABE. In Fifth International Conference on Communication Systems and Network Technologies (CSNT). Gwalior (India), 2015, p. 402–406. DOI: 10.1109/CSNT.2015.46
33. GHADERI, M., SAVOJI, M. H. Wideband speech coding using ADPC Manda new enhanced bandwidth extension method. In IEEE 7th International Symposium on Intelligent Signal Processing (WISP). Floriana (Malta), 2011, p. 1–4. DOI: 10.1109/WISP.2011.6051691
34. ALIPOOR, G., SAVOJI, M. H. Wide-band speech coding based on bandwidth extension and sparse linear prediction. In 35th International Conference on Telecommunications and Signal Processing (TSP). Prague (Czech Republic), 2012, p. 454–459. DOI: 10.1109/TSP.2012.6256335
35. DELFOROUZI, A., POOYAN, M. Adaptive digital audio steganography based on integer wavelet transform. In Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007). Kaohsiung (Taiwan), 2007, p. 283–286. DOI: 10.1109/IIH-MSP.2007.69
36. NIZAMPATNAM, P., RAJU, G. R. L. V. N. S. Transform domain speech bandwidth extension. Circuits, Systems, and Signal Processing, 2019, vol. 38, no. 12, p. 5717–5733. DOI: 10.1007/s00034-019-01142-w
37. SAGI, A., MALAH, D. Bandwidth extension of telephone speech aided by data embedding. EURASIP Journal on Advances in Signal Processing, 2007, p. 1–16. DOI: 10.1155/2007/64921
38. KURADA, P., MARUVADA, S., SANAGAPALLEA, K. R. Speech bandwidth extension using DWT-FFT-based data hiding. Radioengineering, 2020, vol. 29, no. 1, p. 174–181. DOI: 10.13164/re.2020.0174
39. KODURI, S. K., KUMAR, T. DWT-DCT based data hiding for speech bandwidth extension. Radioengineering, 2021, vol. 30, no. 2, p. 435–442. DOI: 10.13164/re.2021.0435
40. HOSODA, Y., KAWAMURA, A., IIGUNI, Y. Speech bandwidth extension using data hiding based on discrete Hartley transform domain. Circuits, Systems, and Signal Processing, 2022, vol. 41, p. 2290–2307. DOI: 10.1007/s00034-021-01893-5
41. PRASAD, N. Speech bandwidth extension using spectral data masking. Cognitive Computing and Cyber Physical Systems, 2025, vol. 598, p. 84–93. DOI: 10.1007/978-3-031-77078-4
42. REKIK, S., GUERCHI, D., SELOUANI, S., et al. Speech steganography using wavelet and Fourier transforms. Journal of Audio, Speech, and Music Processing, 2012, vol. 20, p. 1–14. DOI: 10.1186/1687-4722-2012-20
43. KODURI, S.K., TAPPETA, K.K. Speech bandwidth extension aided by hybrid model transform domain data hiding. In IEEE International Symposium on Circuits and Systems (ISCAS). Sapporo (Japan), 2019, p. 1–5. DOI: 10.1109/ISCAS.2019.8702323
44. HASSAN, A.A., HERSHEY, J.E., SAULNIER, G.J. Perspectives in Spread Spectrum. Boston (MA, USA): Kluwer Academic Publishers, 1998. DOI: 10.1007/978-1-4615-5531-5
45. GOLDSMITH, A. Wireless Communications. Cambridge (U.K.): Cambridge University Press, 2005. DOI: 10.1017/CBO9780511841224
46. PRASAD, N., KISHORE KUMAR, T. Speech bandwidth extension aided by magnitude spectrum data hiding. Circuits, Systems, and Signal Processing, 2017, vol. 36, p. 4512–4540. DOI: 10.1007/s00034-017-0526-5
47. NIZAMPATNAM, P., TAPPETA, K.K. Bandwidth extension of narrowband speech using integer wavelet transform. IET Signal Processing, 2017, vol. 11, no.4, p.437–445.DOI: 10.1049/iet-spr.2016.0453
48. FAROUK, M. H. Application of Wavelets in Speech Processing. Cham (Switzerland): Springer International Publishing, 2013. DOI: 10.1007/978-3-319-02732-6
49. LINGHUI, W., WEI, H., KAIHONG, Z., et al. Adaptive channel equalization based on RLS algorithm. In International Conference on System Science, Engineering Design and Manufacturing Informatization. Guiyang (China), 2011, p. 105–108. DOI: 10.1109/ICSSEM.2011.6081250
50. NTT ADVANCED TECHNOLOGY CORPORATION. Multi Lingual Speech Database for Telephonometry. 2021, [Online]. Available at: https://www.rd.ntt/e/research/HI0002.html
51. DING, H. Wideband audio over narrowband low-resolution media. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Montreal (QC, Canada), 2004, p. 489–492. DOI: 10.1109/ICASSP.2004.1326029
52. LAMBLIN, C., QUINQUIS, C., USAI, P. ITU-T G.722.1 Annex C: The first ITU-T super wideband audio coder. IEEE Communications Magazine, 2008, vol. 46, no. 10, p. 116–122. DOI: 10.1109/MCOM.2008.4644128
53. CHEN, S., LEUNG, H. Concurrent data transmission through analog speech channel using data hiding. IEEE Signal Processing Letters, 2005, vol. 12, no. 8, p. 581–584. DOI: 10.1109/LSP.2005.851259
54. LIU, X., BAO, C. Audio bandwidth extension based on ensemble echo state networks with temporal evolution. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016, vol. 24, no. 3, p. 594–607. DOI: 10.1109/TASLP.2016.2519146
55. PULAKKA, H., LAAKSONEN, L., VAINIO, M., et al. Evaluation of an artificial speech bandwidth extension method in three languages. IEEE Transactions on Audio, Speech, and Language Processing, 2008, vol. 16, no. 6, p. 1124–1137. DOI: 10.1109/TASL.2008.925149
56. ITU-T RECOMMENDATION. Perceptual Evaluation of Speech Quality (PESQ): An Objective Method for End-to-End Speech Quality Assessment of Narrow-Band Telephone Networks and Speech Codecs. Rec. ITU-T P. 862, 2001.
57. KEISER, B. E., STRANGE, E. Digital Telephony and Network Integration. Boston (MA, USA): Springer US, 2012. DOI: 10.1007/978-1-4615-1787-0

Keywords: Curvelet transform, fast Fourier transform, hybrid steganography, speech quality, public switched telephone networks, linear predictive coding

T. I. Unger, M. Kuczmann [references] [full-text] [DOI: 10.13164/re.2025.0603] [Download Citations]
The Impact of Terrain Sampling Density on 5G NR-V2X Downlink Channel Modeling Using Various Propagation Models at the 3.6 GHz Band

This study investigates the sensitivity of radio wave propagation models to terrain sampling density in a 5G New Radio Vehicle-to-Everything downlink scenario at 3.6 GHz. Four widely used models are analysed: the empirical ITU-R P.1546-6, the deterministic Parabolic Equation Method, and the hybrid ITU-R P.1812-6 and ITU-R P.452-16. Real terrain profiles from Hungary are considered at multiple resolutions, allowing a systematic assessment of how accuracy degrades as the representation of terrain becomes oarser. The analysis reveals a consistent ranking across nvironments: the empirical model is the least affected by esolution changes, while deterministic and hybrid methods re significantly more sensitive. To interpret these differences, the study introduces a spectral complexity measure of errain profiles and establishes its strong relationship with error growth through regression analysis. This provides a novel ramework for explaining and quantifying the impact of terrain detail on model behaviour. The findings highlight both he methodological contribution of linking spectral complexity to propagation accuracy and the practical implications or optimising the trade-off between computational efficiency nd prediction reliability in vehicular network planning.

1. CATEDRA, M., PEREZ-ARRIAGA, J. Cell Planning for Wireless Communications. 1st ed. Norwood (USA): Artech House, 1999. ISBN: 978-0-89006-945-9
2. RAPPAPORT, T. S. Wireless Communications: Principles and Practice. 2nd ed. Upper Saddle River (USA): Prentice Hall, 2001. ISBN: 978-0130422323
3. UNGER, T. I., KUCZMANN, M. Comparison of outdoor radio wave propagation models for land mobile systems in the 3.6 GHz and 6 GHz frequency bands. Telecom, 2025, vol. 6, no. 2, p. 1–41. DOI: 10.3390/telecom6020042
4. CAMA-PINTO, D., DAMAS, M., HOLGADO-TERRIZA, J. A., et al. Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions. Sensors, 2020, vol. 20, no. 22, p. 6621–6636. DOI: 10.3390/s20226621
5. HAROUNABADI, M., SOLEYMANI, D. M., BHADAURIA, S., et al. V2X in 3GPP standardization: NR side link in Release-16 and beyond. IEEE Communications Standards Magazine, 2021, vol. 5, no. 1, p. 12–21. DOI: 10.1109/MCOMSTD.001.2000070
6. AL HARTHI, F. R. A., TOUZENE, A., ALZIDI, N., et al. Intelligent handover decision-making for vehicle-to-everything (V2X) 5G networks. Telecom, 2025, vol. 6, no. 3, p. 1–27. DOI: 10.3390/telecom6030047
7. WAQAS, S. M., TANG, Y., YU, L., ABBAS, F. A joint cluster-based RRM and low-latency framework using the full-duplex mechanism for NR-V2X networks. Computer Communications, 2023, vol. 209, p. 513–525. DOI: 10.1016/j.comcom.2023.07.032
8. TARIQ, S., AL-RIZZO, H., HASAN, M. N., et al. Stochastic versus ray tracing wireless channel modeling for 5G and V2X applications: opportunities and challenges. Antenna Systems, 2021, p. 1–16. DOI: 10.5772/intechopen.101625
9. LI, J., TAYLOR, G., KIDNER, D. B. Accuracy and reliability of map-matched GPS coordinates: The dependence on terrain model resolution and interpolation algorithm. Computers & Geosciences, 2005, vol. 31, no. 2, p. 241–251. DOI: 10.1016/j.cageo.2004.06.011
10. EUROPEAN COMMISSION. Commission Implementing Decision 2014/276/EU of 2 May 2014 Amending Decision 2008/411/EC on the Harmonisation of the 3400–3800 MHz Frequency Band for Terrestrial Systems Capable of Providing Electronic Communications Services in the Community. [Online] Cited 2025-07-21. Available at: https://eur-lex.europa.eu/eli/dec_impl/2014/276/oj
11. ELECTRONIC COMMUNICATIONS COMMITTEE (ECC). ECC Decision (11)06 of 9 December 2011 on Harmonised Frequency Arrangements for Mobile/Fixed Communications Networks (MFCN) Operating in the Band 3400–3800 MHz. [Online] Cited 2025-07-21. Available at: https://docdb.cept.org/document/433
12. RADIOCOMMUNICATION SECTOR OF INTERNATIONAL TELECOMMUNICATION UNION (ITU-R). Radio Regulations 2024. [Online] Cited 2025-07-21. Available at:
13. http://handle.itu.int/11.1002/pub/8229633e-en
14. ELECTRONIC COMMUNICATIONS COMMITTEE (ECC) WITHIN THE EUROPEAN CONFERENCE OF POSTAL AND TELECOMMUNICATIONS ADMINISTRATIONS (CEPT). The European Table of Frequency Allocations in the Frequency Range 8.3 kHz to 3000 GHz (ECA Table) Approved January 2025. [Online] Cited 2025-02-06. Available at:
15. https://efis.cept.org/sitecontent.jsp?sitecontent=ecatable
16. LUSVARGHI, L., MERANI, M. L. MoreV2X – A new radio vehicular communication module for ns-3. In Proceedings of the 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). Norman (OK, USA), 2021, p. 1–7. DOI: 10.1109/VTC2021-Fall52928.2021.9625478
17. ZHAO, Y., YANG, C., JI, T. Propagation characteristics of 3.6 GHz typical application scenarios based on parabolic equation [in Chinese].
18. Dianbo Kexue Xuebao / Chinese Journal of Radio Science, 2021, vol. 36, no. 4, p. 604–610. DOI: 10.12265/j.cjors.2020227
19. AN, H., GUAN, K., WANG, X., et al. Vehicle-to-vehicle channel measurements and power domain modeling in mountainous plateau environments for emergency communications. IEEE Transactions on Intelligent Transportation Systems, 2024, vol. 25, no. 12, p. 21060–21073. DOI: 10.1109/TITS.2024.3465014
20. YANG, C.,WANG, J., YOU, X., et al. Applicability of ITU-R P.1546 recommendation in typical terrestrial areas of China [in Chinese]. Dianbo Kexue Xuebao / Chinese Journal of Radio Science, 2019, vol. 34, no. 3, p. 295–301. DOI: 10.13443/j.cjors.2018020801
21. KALLIOVAARA, J., EKMAN, R., JOKELA, T., et al. Suitability of ITU-R P.1546 propagation predictions for allocating LTE SDL with GE06. In Proceedings of the 2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). Cagliari (Italy), 2017, p. 1–6. DOI: 10.1109/BMSB.2017.7986174
22. OSTLIN, E., SUZUKI, H., ZEPERNICK, H.-J. Evaluation of the propagation model recommendation ITU-R P.1546 for mobile services in rural Australia. IEEE Transactions on Vehicular Technology, 2008, vol. 57, no. 1, p. 38–51. DOI: 10.1109/TVT.2007.901902
23. LEVY, M. F. Parabolic equation modelling of propagation over irregular terrain. Electronics Letters, 1990, vol. 26, no. 15, p. 1153–1155.
24. DOI: 10.1049/el:19900746
25. DONOHUE, D. J., KUTTLER, J. R. Propagation modeling over terrain using the parabolic wave equation. IEEE Transactions on Antennas and Propagation, 2000, vol. 48, no. 2, p. 260–277. DOI: 10.1109/8.833076
26. HOLM, P. D. Wide-angle shift-map PE for a piecewise linear terrain. In: AIP Conference Proceedings, 2009, vol. 1106, p. 56–65. DOI: 10.1063/1.3117113
27. JANASWAMY, R. A Curvilinear coordinate-based split-step parabolic equation method for propagation predictions over terrain. IEEE Transactions on Antennas and Propagation, 1998, vol. 46, no. 7, p. 1089–1097. DOI: 10.1109/8.704813
28. WEI, Q.-F., YIN, C.-Y., FAN, Q.-M. Research and verification for parabolic equation method of radio wave propagation in obstacle environment
29. [in Chinese]. Wuli Xuebao / Acta Physica Sinica, 2017, vol. 66, no. 12, p. 1–7. DOI: 10.7498/aps.66.124102
30. OCHI, G. M., SWEARINGEN, M. E. Parabolic equation comparisons with Galerkin discretization and boundary fitted grid for modeling infrasound propagation: 2D versus 3D. Proceedings of Meetings on Acoustics, 2021, vol. 45, no. 1, p. 1–13. DOI: 10.1121/2.0001551
31. SILVA, M. A. N., COSTA, E., LINIGER, M. Analysis of the effects of irregular terrain on radio wave propagation based on a three-dimensional parabolic equation. IEEE Transactions on Antennas and Propagation, 2012, vol. 60, no. 4, p. 2138–2143. DOI: 10.1109/TAP.2012.2186227
32. LI, J., WAGEN, J.-F., LACHAT, E. ITU model for multi knife-edge diffraction. IEE Proceedings: Microwaves, Antennas and Propagation, 1996, vol. 143, no. 6, p. 539–541. DOI: 10.1049/ip-map:19960884
33. LU, J. S., HAN, X., BERTONI, H. L. The influence of terrain scattering on radio links in hilly/mountainous regions. IEEE Transactions on Antennas and Propagation, 2013, vol. 61, no. 3, p. 1385–1395. DOI: 10.1109/TAP.2012.2231919
34. LI, X., WU, Z., WANG, S., JIAO, H. Path loss and blockage modeling for vehicle-to-vehicle channel above 6 GHz. In Proceedings of the 2018 IEEE 4th International Conference on Computer and Communications (ICCCC 2018). Chengdu (China), 2018, p. 494–499. DOI: 10.1109/CompComm.2018.8780963
35. BOBAN, M., BARROS, J., TONGUZ, O. K. Geometry-based vehicle-to-vehicle channel modeling for large-scale simulation. IEEE Transactions on Vehicular Technology, 2014, vol. 63, no. 9, p. 4146–4164. DOI: 10.1109/TVT.2014.2317803
36. GONG, Y., WANG, S., ZHANG, Y., et al. VANETs LTE-V performance evaluation using 3D geometry-stochastic channel model. In Proceedings of the 18th COTA International Conference of Transportation Professionals (CICTP 2018): Intelligence, Connectivity, and Mobility. Beijing (China), 2018, p. 2778–2787. DOI: 10.1061/9780784481523.276
37. RADIOCOMMUNICATION SECTOR OF INTERNATIONAL TELECOMMUNICATION UNION (ITU-R). Recommendation ITU-R P.1546-6: Method for Point-to-Area Predictions for Terrestrial Services in the Frequency Range 30 MHz to 4000 MHz. [Online] Cited 2025-07-21. Available at: https://www.itu.int/rec/R-RECP.
38. 1546-6-201908-I/en
39. RASOOL, H. F., QURESHI, M. A., AZIZ, A., et al. A. An introduction to the parabolic equation method for electromagnetic wave propagation in tunnels. COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2022, vol. 41, no. 5, p. 1313–1331. DOI: 10.1108/COMPEL-07-2021-0245
40. CRANK, J., NICOLSON, P. A practical method for numerical evaluation of solutions of partial differential equations of the heat-conduction type. Mathematical Proceedings of the Cambridge Philosophical Society, 1947, vol. 43, no. 1, p. 50–67. DOI: 10.1017/S0305004100023197
41. LEVY, M. Parabolic Equation Methods for Electromagnetic Wave Propagation. 1st ed. London (UK): The Institution of Electrical Engineers, 2000. ISBN: 978-0852967645
42. RADIOCOMMUNICATION SECTOR OF INTERNATIONAL TELECOMMUNICATION UNION (ITU-R). Recommendation ITU-R P.452-16: Prediction Procedure for the Evaluation of Interference Between Stations on the Surface of the Earth at Frequencies Above About 0.1 GHz. [Online] Cited 2025-07-21. Available at: https://www.itu.int/rec/R-REC-P.452-16-201507-S/en
43. RADIOCOMMUNICATION SECTOR OF INTERNATIONAL TELECOMMUNICATION UNION (ITU-R). Recommendation ITU-R P.1812-6: A Path-Specific Propagation Prediction Method for Point-to-Area Terrestrial Services in the Frequency Range 30 MHz to 6000 MHz. [Online] Cited 2025-07-21. Available at: https://www.itu.int/rec/R-REC-P.1812-6-202109-S/en
44. LS TELCOM AG. CHIRplus_BC–Broadcast Network Planning Tool. [Online] Cited 2025-07-22. Available at: https://www.lstelcom.com/en/products/network-planningtools/broadcast-mobile-tv/
45. OPENTOPOGRAPHY. Copernicus GLO-30 Digital Elevation Model. [Online] Cited 2025-07-22. Available at: https://portal. opentopography.org/raster?opentopoID=OTSDEM.032021.4326.3
46. THE MATHWORKS, INC. MATLAB, Version R2023b. [Online] Cited 2025-07-22. Available at: https://www.mathworks.com/products/matlab.html
47. INTERNATIONAL TELECOMMUNICATION UNION. Software, Data and Validation Examples for Ionospheric and Tropospheric Radio Wave Propagation and Radio Noise. [Online] Cited 2025-07-22. Available at: https://www.itu.int/en/ITU-R/studygroups/rsg3/Pages/iono-tropo-spheric.aspx
48. OZGUN, O., SAHIN, V., ERGUDEN, M. E., et al. PETOOL v2.0: Parabolic equation toolbox with evaporation duct models and real environment
49. data. Computer Physics Communications, 2020, vol. 256, p. 1–9. DOI: 10.1016/j.cpc.2020.107454
50. CURRY, T., ABBAS, R. 5G coverage, prediction, and trial measurements. arXiv Preprint, 2020, p. 1–4. DOI: 10.48550/arXiv.2003.09574
51. WACKERLY, D., MENDENHALL,W., SCHEAFFER, R. L. Mathematical Statistics with Applications. 7th ed. Boston (USA): Cengage Learning, 2014. ISBN: 978-0-495-11081-1
52. AGUILAR, F. J., AGUILAR, M. A., AGUERA, F., et al. Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogrammetric Engineering & Remote Sensing, 2005, vol. 71, no. 7, p. 805–816. DOI: 10.14358/PERS.71.7.805
53. OPPENHEIM, A. V., WILLSKY, A. S., NAWAB, S. H. Signals and Systems. 2nd ed. Upper Saddle River (NJ, USA): Prentice Hall, 1997. ISBN: 0-13-8147574

Keywords: Outdoor wave propagation models, terrain sensitivity, radio frequency electromagnetic waves, path loss prediction, channel modeling, vehicle-to-everything communications

X. Yao, Z. Xu, F. Qiang [references] [full-text] [DOI: 10.13164/re.2025.0624] [Download Citations]
High-Precision Indoor Localization via Dual-Modal AOA/TOA Fusion with Deep Learning and Particle Filters

As the era of IoT and artificial intelligence advances, the demand for high-precision indoor positioning systems continues to grow. Achieving accurate positioning in indoor environments remains challenging due to the presence of obstacles and signal interference, especially in Non-Line-of-Sight (NLOS) conditions. To address these challenges, this paper proposes a novel indoor positioning algorithm based on the fusion of Angle of Arrival (AOA) and Time of Arrival (TOA) data. A hybrid model combining Asymptotic Gradient Boosted Regression Trees (GBRT) and Elastic Net (EN) is used to reduce AOA measurement errors in NLOS environments, followed by the application of the Levenberg-Marquardt (LM) optimization algorithm to enhance localization accuracy. Experimental results show a significant reduction in positioning error, with an average error of 0.47 meters, representing a 41.25% improvement compared to the KF+WLS algorithm. Meanwhile, to improve TOA positioning, a deep learning-based TOA fingerprinting algorithm is proposed, this algorithm captures complex spatiotemporal features in TOA data, leading to a 25.00% and 15.22% reduction in root mean square error (RMSE) compared to the WKNN and WLS algorithms, respectively. Finally, a fusion strategy based on Particle Filtering (PF) is introduced to combine AOA and TOA data, achieving further RMSE reductions of 35.42% and 20.51%, compared to individual AOA and TOA methods.

1. TIMOFEEV, A. L., SULTANOV, A. K., MESHKOV, I. K., et al. Increasing the positioning accuracy of the GLONASS system. Siberian Aerospace Journal, 2024, vol. 25, no. 4, p. 482–492. DOI: 10.31772/2712-8970-2024-25-4-482-492
2. LI, G. D., JIN, J., WANG, F., et al. Efficient path planning algorithm using topology for indoor environment (in Chinese). Computer Engineering, 2022, vol. 48, p. 95–106. DOI: 10.19678/j.issn.1000-3428.0061761
3. ASAAD, S. M., MAGHDID, H. S. A comprehensive review of indoor/outdoor localization solutions in IoT era: Research challenges and future perspectives. Computer Networks, 2022, vol. 212, p. 1–28. DOI: 10.1016/j.comnet.2022.109041
4. FARAHSARI, P. S., FARAHZADI, A., REZAZADEH, J., et al. A survey on indoor positioning systems for IoT-based applications. IEEE Internet of Things Journal, 2022, vol. 9, no. 10, p.7680–7699. DOI: 10.1109/JIOT.2022.3149048
5. ROY, P., CHOWDHURY, C. A survey on ubiquitous WiFi-based indoor localization system for smartphone users from implementation perspectives. CCF Transactions on Pervasive Computing and Interaction, 2022, vol. 4, no. 3, p. 298–318. DOI: 10.1007/s42486-022-00089-3
6. PANJA, A. K., KARIM, S. F., NEOGY, S., et al. Improving the sustainability of WiFi-enabled indoor localization systems through meta-heuristic based instance selection approach. Expert Systems with Applications, vol. 257, p. 1–14. DOI: 2024, 10.1016/j.eswa.2024.125063
7. RIZZI, M., FERRARI, P., FLAMMINI, A., et al. Evaluation of the IoT LoRaWAN solution for distributed measurement applications. IEEE Transactions on Instrumentation and Measurement, 2017, vol. 66, no. 12, p. 3340–3349. DOI: 10.1109/TIM.2017.2746378
8. MORADBEIKIE, A., ZARE, M., KESHAVARZ, A., et al. RSSI based LoRaWAN dataset collected in a dynamic and harsh industrial environment with high humidity. Data in Brief, 2024, vol. 53, p. 1–8. DOI: 10.1016/j.dib.2024.110120
9. DALVEREN, Y., KARA, A. Multipath exploitation in emitter localization for irregular terrains. Radioengineering, 2019, vol. 28, no. 2, p. 473–482. DOI: 10.13164/re.2019.0473
10. WU, P. Comparison between the ultra-wide band based indoor positioning technology and other technologies. Journal of Physics: Conference Series, 2022, vol. 2187, p. 1–8. DOI: 10.1088/17426596/2187/1/012010
11. LIU, Z., CHEN, L., ZHOU, X., et al. Machine learning for time-of arrival estimation with 5G signals in indoor positioning. IEEE Internet of Things Journal, 2023, vol. 10, no. 11, p. 9782–9795. DOI: 10.1109/JIOT.2023.3234123
12. WAN, Q., WU, T., ZHANG, K., et al. A high precision indoor positioning system of BLE AOA based on ISSS algorithm. Measurement, 2024, vol. 224, p.
13. 1–16. DOI: 10.1016/j.measurement.2023.113801
14. FURFARI, F., GIROLAMI, M., MAVILIA, F., et al. Indoor localization algorithms based on angle of arrival with a benchmark comparison. Ad Hoc Networks, 2025, vol. 166, p. 1–16. DOI: 10.1016/j.adhoc.2024.103691
15. ZHANG, Z., WANG, D., YANG, B., et al. Weighted multidimensional scaling localization method with bias reduction based on TOA. IEEE Sensors Journal, 2023, vol. 23, no. 17, p. 19803–19814. DOI: 10.1109/JSEN.2023.3296986
16. ZAIDI, M., BOUAZZI, I., USMAN, M., et al. Cooperative scheme ToA‐RSSI and variable anchor positions for sensors localization in 2D environments. Complexity, 2022, no. 1, p. 1–11 DOI: 10.1155/2022/5069254
17. WANG, W., WANG, G., HO, K. C., et al. Robust TDOA localization based on maximum correntropy criterion with variable center. Signal Processing, 2023, vol. 205, p. 1–9. DOI: 10.1016/j.sigpro.2022.108860
18. ÇELIK, E. IEGQO-AOA: Information-exchanged Gaussian arithmetic optimization algorithm with quasi-opposition learning. Knowledge-Based Systems, 2023, vol. 260, p. 1–18. DOI: 10.1016/j.knosys.2022.110169
19. SHANG, S., WANG, L. Overview of WiFi fingerprinting‐based indoor positioning. IET Communications, 2022, vol. 16, no. 7, p. 725–733. DOI: 10.1049/cmu2.12386
20. CAO, H., WANG, Y., BI, J., et al. LOS compensation and trusted NLOS recognition assisted WiFi RTT indoor positioning algorithm. Expert Systems with Applications, 2024, vol. 243, p. 1–12. DOI: 10.1016/j.eswa.2023.122867
21. CHEN, X., FENG, Z., WEI, Z., et al. Multiple signal classification based joint communication and sensing system. IEEE Transactions on Wireless Communications, 2023, vol. 22, no. 10, p. 6504–6517. DOI: 10.1109/TWC.2023.3244195
22. GAO, F., GERSHMAN, A. B. A generalized ESPRIT approach to direction-of-arrival estimation. IEEE Signal Processing Letters, 2005, vol. 12, no. 3, p. 254–257. DOI: 10.1109/LSP.2004.842276
23. DENG, W., LI, J., TANG, Y., et al. Low-complexity joint angle of arrival and time of arrival estimation of multipath signal in UWB system. Sensors, 2023, vol. 23, no. 14, p. 1–15. DOI: 10.3390/s23146363
24. WANG, K., LI, B. Research on AOA/Snell indoor fusion positioning algorithm with NLOS error compensation. In 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). Chongqing (China), 2024, p. 332–336. DOI: 10.1109/IAEAC59436.2024.10504027
25. KOIVISTO, M., TALVITIE, J., COSTA, M., et al. Joint cm wave based multiuser positioning and network synchronization in dense 5G networks. In IEEE Wireless Communications and Networking Conference (WCNC). Barcelona (Spain), 2018, p. 1–6. DOI: 10.1109/WCNC.2018.8377435
26. XIAO, K., HAO, F., ZHANG, W., et al. Research and implementation of indoor positioning algorithm based on Bluetooth 5.1 AOA and AOD. Sensors, 2024, vol. 24, no. 14, p. 1–17. DOI: 10.3390/s24144579
27. LIU, J., WANG, T., LI, Y., et al. A transformer-based signal denoising network for AoA estimation in NLoS environments. IEEE Communications Letters, 2022, vol. 26, no. 10, p. 2336–2339. DOI: 10.1109/LCOMM.2022.3187661
28. FENG, Y. L., LI, M. Z., LI, J., et al. Edge cloud resource scheduling with deep reinforcement learning. Radioengineering, 2025, vol. 34, no. 1. p. 92–108. DOI: 10.13164/re.2025.0092
29. LIU, G., KANG, Y., QUAN, H., et al. The detection performance of the dual-sequence-frequency-hopping signal via stochastic resonance processing under color noise. Radioengineering, 2019, vol. 27, no. 3, p. 618–626. DOI: 10.13164/re.2019.0618
30. ZHOU, Z., LI, S., LIU, Y. Study on AOA location algorithm based on machine learning optimization. In International Conference on Microwave and Millimeter Wave Technology (ICMMT). Bejing (China), 2024, vol. 1, p. 1–3. DOI: 10.1109/ICMMT61774.2024.10671996
31. SCHANTZ, H. The Art and Science of Ultrawideband Antennas. 2nd ed. Beijing (China): Posts & Telecom Press. 2012, ISBN: 9787115262110
32. ZHAO, R., ZHANG, H., LIN, Q., et al. UWB Positioning Technology and Its Applications in Smart Manufacturing. (in Chinese) Beijing (China): China Machine Press, 2020. ISBN: 9787111667445

Keywords: Indoor localization, angle of arrival, time of arrival.machine learning, particle filter

X. Dai, J. Zhao, Z. Shen, H. Hong, G. Li [references] [full-text] [DOI: 10.13164/re.2025.0641] [Download Citations]
A Broadband Low-Profile Dual-Polarized Antenna Based on a Metasurface

This article presents a wideband low-profile dual-polarization antenna based on a metasurface (MS). The antenna consists of a 4×4 metasurface array made up of 16 circular metal patches, a radiating patch, and a metallic ground plane. The metasurface excites the surface waves of the radiating patch to generate multiple resonant points, thereby broadening the antenna bandwidth. In addition, two rectangular cross-slot structures at the center of the patch enhance port isolation. Finally, a prototype with dimensions of 46 mm × 46 mm × 3.2 mm (1.28λ₀ × 1.28λ₀ × 0.88λ₀) was fabricated. Measurement results show that the antenna achieves a return loss better than -10 dB and an isolation better than -16 dB within the operating bandwidth of 6.38–10.3 GHz. It can be concluded that the experimental results of the antenna are in good agreement with the simulation results.

1. CHEN, C. A single-layer single-patch dual-polarized high-gain cross-shaped microstrip patch antenna. IEEE Antennas and Wireless Propagation Letters, 2023, vol. 22, no. 10, p. 2417–2421. DOI: 10.1109/LAWP.2023.3289861
2. KEDZE, K. E., WANG, H., I, PARK, I. A metasurface-based wide bandwidth and high-gain circularly polarized patch antenna. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 1, p. 732–737. DOI: 10.1109/TAP.2021.3098574
3. CHEN, Z., TIAN, J., LIU, H., et al. Novel pattern-diverse millimeter-wave antenna with broadband, high-gain, enhanced coverage for energy-efficient unmanned aerial vehicle. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 5, p. 4081–4087. DOI: 10.1109/TVT.2021.3071483
4. GUO, J., LIU, F, ZHAO, L., et al. A dual-polarized patch antenna with high isolation. In 2019 International Symposium on Antennas and Propagation (ISAP). Xi'an (China), 2019, p. 1–3.
5. WANG, Z., LIU, J., LONG, Y. A simple wide-bandwidth and high gain microstrip patch antenna with both sides shorted. IEEE Antennas and Wireless Propagation Letters, 2019, vol. 18, no. 6, p. 1144–1148. DOI: 10.1109/LAWP.2019.2911045
6. TA, S. X., PARK, I. Compact wideband circularly polarized patch antenna array using metasurface. IEEE Antennas and Wireless Propagation Letters, 2017, vol. 16, p. 1932–1936. DOI: 10.1109/LAWP.2017.2689161
7. CAO, Y., CAI, Y., CAO, W., et al. Broadband and high-gain microstrip patch antenna loaded with parasitic mushroom-type structure. IEEE Antennas and Wireless Propagation Letters, 2019, vol. 18, no. 7, p. 1405–1409. DOI: 10.1109/LAWP.2019.2917909
8. LIU, W., CHEN, Z. N., QING, X. Metamaterial-based low-profile broadband aperture-coupled grid-slotted patch antenna. IEEE Transactions on Antennas and Propagation, 2015, vol. 63, no. 7, p. 3325–3329. DOI: 10.1109/TAP.2015.2429741
9. LIU, W., CHEN, Z. N., QING, X. Metamaterial-based low-profile broadband mushroom antenna. IEEE Transactions on Antennas and Propagation, 2014, vol. 62, no. 3, p. 1165–1172. DOI: 10.1109/TAP.2013.2293788
10. HUSSAIN, N., JEONG, M. J., PARK, J., et al. A broadband circularly polarized Fabry-Perot resonant antenna using a single layered PRS for 5G MIMO applications. IEEE Access, 2019, vol. 7, p. 42897–42907. DOI: 10.1109/ACCESS.2019.2908441
11. LIU, Z. G., LU, W. B. Low-profile design of broadband high gain circularly polarized Fabry-Perot resonator antenna and its array with linearly polarized feed. IEEE Access, 2017, vol. 5, p. 7164–7172. DOI: 10.1109/ACCESS.2017.2675378
12. SARIN, V. P., NISHAMOL, M. S, TONY, D., et al. A wideband stacked offset microstrip antenna with improved gain and low cross polarization. IEEE Transactions on Antennas and Propagation, 2011, vol. 59, no. 4, p. 1376–1379. DOI: 10.1109/TAP.2011.2109362
13. WI, S. H., LEE, Y. S., YOOK, J.-G. Wideband microstrip patch antenna with u-shaped parasitic elements. IEEE Transactions on Antennas and Propagation, 2007, vol. 55, no. 4, p. 1196–1199. DOI: 10.1109/TAP.2007.893427
14. SHAFAI, L. Slotted microstrip patch antenna and its influence on wideband planar antenna designs. In 2020 IEEE Asia-Pacific Microwave Conference (APMC). Hong Kong, 2020, p. 360–362. DOI: 10.1109/APMC47863.2020.9331537
15. DAS, A., MOHANTY, M. N., MISHRA, R. K. Optimized design of H-slot antenna for bandwidth improvement. In 2015 IEEE Power, Communication and Information Technology Conference (PCITC). Bhubaneswar (India), 2015, p. 563–567. DOI: 10.1109/PCITC.2015.7438228
16. GUO, Y. X., LUK, K. M., LEE, K. F. Broadband dual polarization patch element for cellular-phone base stations. IEEE Transactions on Antennas and Propagation, 2002, vol. 50, no. 2, p. 251–253. DOI: 10.1109/8.998004
17. MAK, K. M., LAI, H. W., LUK, K. M. Wideband dual polarized antenna fed by L- and M-probe. In 2012 Asia Pacific Microwave Conference Proceedings. Kaohsiung (Taiwan), 2012, p. 1058 to 1060. DOI: 10.1109/APMC.2012.6421824
18. LAI, H. W., LUK, K. M. Dual polarized patch antenna fed by meandering probes. IEEE Transactions on Antennas and Propagation, 2007, vol. 55, no. 9, p. 2625–2627. DOI: 10.1109/TAP.2007.904158
19. MOSALLAEI, H., SARABANDI, K. Antenna miniaturization and bandwidth enhancement using a reactive impedance substrate. IEEE Transactions on Antennas and Propagation, 2004, vol. 52, no. 9, p. 2403–2414. DOI: 10.1109/TAP.2004.834135
20. COSTA, F., LUUKKONEN, O., SIMOVSKI, C. R., et al. TE surface wave resonances on high-impedance surface based antennas: Analysis and modeling. IEEE Transactions on Antennas and Propagation, 2011, vol. 59, no. 10, p. 3588–3596. DOI: 10.1109/TAP.2011.2163750
21. WU, B., JI, P., LI, Y., et al. A microstrip antenna achieves dual polarization conversion by rotating metasurface. IEEE Access, 2024, vol. 12, p. 121387–121394. DOI: 10.1109/ACCESS.2024.3452974
22. HE, Y., LI, Y., SUN, W., et al. Dual linearly polarized microstrip antenna using a slot-loaded TM50 mode. IEEE Antennas and Wireless Propagation Letters, 2018, vol. 17, no. 12, p. 2344–2348. DOI: 10.1109/LAWP.2018.2874472
23. AFSHANI, A., WU, K. Dual-polarized patch antenna excited concurrently by a dual-mode substrate integrated waveguide. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 3, p. 2322–2327. DOI: 10.1109/TAP.2021.3118816
24. LUO, Y., CHEN, Z. N., MA, K. A single-layer dual-polarized differentially fed patch antenna with enhanced gain and bandwidth operating at dual compressed high-order modes using characteristic mode analysis. IEEE Transactions on Antennas and Propagation. 2020, vol. 68, no. 5, p. 4082–4087. DOI: 10.1109/TAP.2019.2951536

Keywords: Dual-polarization, metasurface, low-profile, broadband

J. Zhang, K. Wang, J. Wang, H. Wei, J. Chang, J. Yao, L. Zhang [references] [full-text] [DOI: 10.13164/re.2025.0648] [Download Citations]
Hybrid Small-Signal Modeling of GaN HEMT Enhanced by the Integration of SVD and RIME Optimization

For the 20-element small-signal model of GaN HEMT, a combination of the singular value decomposition (SVD) algorithm and the frost ice optimization algorithm is proposed in this paper to extract and optimize the intrinsic parameters of the small-signal model. When the traditional algorithm is employed for parameter extraction, issues of low extraction accuracy and efficiency are encountered. By introducing an optimization algorithm for parameter extraction, the accuracy and efficiency of the process are enhanced. However, previous studies have focused on improving the optimization algorithm to optimize the eigenparameters of GaN HEMT without taking into account the correlation among the parameters within the model. In this study, the SVD algorithm is utilized to process the real and imaginary parts of the intrinsic model Y-parameters, thereby strengthening the correlation between the intrinsic parameters. Subsequently, the new intrinsic model Y-parameters and the RIME algorithm are employed to extract the intrinsic parameters. The experimental results demonstrate that the combination of the SVD algorithm and the frost ice optimization algorithm breaks the isolation between the eigenparameters, improves the parameter correlation, and can accurately extract and optimize the eigenparameters of the small-signal model within the frequency range of 0.5 - 20.5 GHz.

1. ISLAM, N., MOHAMED, M. F. P., KHAN, M. F. A. J., et al. Reliability, applications and challenges of GaN HEMT technology for modern power devices: A review. Crystals, 2022, vol. 12, no. 11, p. 1–42. DOI: 10.3390/cryst12111581
2. PECHEUX, R., KABOUCHE, R., DOGMUS, E., et al. Importance of buffer configuration in GaN HEMTs for high microwave performance and robustness. In 2017 47th European Solid-State Device Research Conference (ESSDERC). Leuven (Belgium), 2017, p. 228–231. DOI: 10.1109/ESSDERC.2017.8066633
3. HUSNA HAMZA, K., NIRMAL, D. A review of GaN HEMT broadband power amplifiers. AEU - International Journal of Electronics and Communications, 2020, vol. 116, p. 1–11. DOI: 10.1016/j.aeue.2019.153040
4. LUONG, D. M., TRAN, X. N. An independently biased 3-stacked GaN HEMT power amplifier for next-generation wireless communication systems. Radioengineering, 2020, vol. 29, no. 4, p. 617–624. DOI: 10.13164/re.2020.0617
5. LIU, R., MEI, B., SU, Y., et al. The effects and mechanisms of 2 MeV proton irradiation on high bias conditions of InP/InGaAs DHBTs. Solid-State Electronics, 2024, vol. 212, p. 1–6. DOI: 10.1016/j.sse.2023.108832
6. ZHANG, J., MEI, B., SU, Y., et al. Influence of BCB protection on irradiation response of InP-based HEMTs: A comparative study. IEEE Transactions on Electron Devices, 2023, vol. 70, no. 8, p. 4225–4230. DOI: 10.1109/TED.2023.3287816
7. ZHANG, C., SU, Y., MEI, B., et al. Effect of proton irradiation on interfacial and electrical performance of N+Np+ InP/InGaAs hetero-junction. Current Applied Physics, 2023, vol. 48, p. 47–52. DOI: 10.1016/j.cap.2023.01.013
8. KOTECHA, R. M., ZHANG, Y., RASHID, A., et al. A physics based compact device model for GaN HEMT power devices. In 2016 IEEE 4th Workshop on Wide Bandgap Power Devices and Applications (WiPDA). Fayetteville (AR, USA), 2016, p. 108–113. DOI: 10.1109/WiPDA.2016.7799919
9. HUSAIN, S., HASHMI, M., GHANNOUCHI, F. M. Comprehensive investigation and comparative analysis of machine learning-based small-signal modelling techniques for GaN HEMTs. IEEE Journal of the Electron Devices Society, 2022, vol. 10, p. 1015–1032. DOI: 10.1109/JEDS.2022.3224433
10. HUSAIN, S., JARNDAL, A., HASHMI, M., et al. Accurate, efficient and reliable small-signal modeling approaches for GaN HEMTs. IEEE Access, 2023, vol. 11, p. 106833–106846. DOI: 10.1109/ACCESS.2023.3317530
11. ZHU, G., CHANG, C., XU, Y., et al. A millimeter-wave scalable small-signal modeling approach based on FW-EM for AlGaN/GaN HEMT up to 110 GHz. Microwave and Optical Technology Letters, 2021, vol. 63, no. 8, p. 2145–2152. DOI: 10.1002/mop.32404
12. TANG, X., YANG, T., JIA, Y., et al. FW-EM-based approach for scalable small-signal modeling of GaN HEMT with consideration of temperature-dependent resistances. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2021, vol. 34, no. 5, p. 1–10. DOI: 10.1002/jnm.2882
13. ISSAOUN, A., ROEDLE, T. Simple small-signal HEMT model suitable for GaN stability analysis and technologies benchmarking. Journal of Applied Physics, 2021, vol. 6, no. 1, p. 1–8. DOI: 10.11648/j.wjap.20210601.11
14. AAMIR AHSAN, S., PAMPORI, A.-H., GHOSH, S., et al. A new small-signal parameter extraction technique for large gate periphery GaN HEMTs. IEEE Microwave and Wireless Components Letters, 2017, vol. 27, no. 10, p. 918–920. DOI: 10.1109/LMWC.2017.2746661
15. CHEN, Y., XU, Y., LUO, Y., et al. A reliable and efficient small signal parameter extraction method for GaN HEMTs. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2020, vol. 33, no. 3, p. 1–14. DOI: 10.1002/jnm.2540
16. CHIGAEVA, E., WALTHES, W., WIEGNER, D., et al. Determination of small-signal parameters of GaN-based HEMTs. In Proceedings 2000 IEEE/Cornell Conference on High Performance Devices (Cat. No.00CH37122). Ithaca (NY, USA), 2000, p. 115–122. DOI: 10.1109/CORNEL.2000.902526
17. KHUSRO, A., HUSAIN, S., HASHMI, M. S., et al. A generic and efficient globalized kernel mapping-based small-signal behavioral modeling for GaN HEMT. IEEE Access, 2020, vol. 8, p. 195046 to 195061. DOI: 10.1109/ACCESS.2020.3033788
18. COLANGELI, S., CICCOGNANI, W., CLERITI, R., et al. Optimization-based approach for scalable small-signal and noise model extraction of GaN-on-SiC HEMTs. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2017, vol. 30, no. 1, p. 1–16. DOI: 10.1002/jnm.2135
19. HUSSEIN, A. S., JARNDAL, A. H. Reliable particle-swarm optimization based parameter extraction method applied to GaN HEMTs. In 2016 16th Mediterranean Microwave Symposium (MMS). Abu Dhabi (United Arab Emirates), 2016, p. 1–4. DOI: 10.1109/MMS.2016.7803850
20. ABUSHAWISH, A., JARNDAL, A. Comparison of GA, GWO, and HHO optimization techniques for modeling substrate/buffer loading effect on GaN HEMTs. In 2021 14th International Conference on Developments in eSystems Engineering (DeSE). Sharjah (United Arab Emirates), 2021, p. 376–381. DOI:
21. 10.1109/DeSE54285.2021.9719328
22. ZHANG, J., HOU, X., LIU, M., et al. Hybrid small-signal modeling of GaN HEMTs based on improved genetic algorithm. Microelectronics Journal, 2022, vol. 127, p. 1–9. DOI: 10.1016/j.mejo.2022.105513
23. WANG, S., ZHANG, J., YANG, S., et al. Hybrid small-signal model parameter extraction for GaN HEMT based on QGA. International Journal of Electronics, 2023, vol. 111, no. 4, p. 729 to 747. DOI: 10.1080/00207217.2023.2188610
24. HUANG, F. Y., TANG, X. S., WEI, Z. N., et al. An improved small-signal equivalent circuit for GaN high-electron mobility transistors. IEEE Electron Device Letters, 2016, vol. 37, no. 11, p. 1399–1402. DOI: 10.1109/LED.2016.2609462
25. CRUPI, G., CADDEMI, A., SCHREURS, D. M. M.-P., et al. The large world of FET small-signal equivalent circuits (invited paper): FET small-signal equivalent circuits. International Journal of RF and Microwave Computer Aided Engineering, 2016, vol. 26, no. 9, p. 749–762. DOI: 10.1002/mmce.21028
26. WEISER, M. C. J., HUCKELHEIM, J., KALLFASS, I. A Novel approach for the modeling of the dynamic ON-State resistance of GaN-HEMTs. IEEE Transactions on Electron Devices, 2021, vol. 68, no. 9, p. 4302–4309. DOI: 10.1109/TED.2021.3098498
27. CRUPI, G., SCHREURS, D. M. M.-P., CADDEMI, A., et al. High-frequency extraction of the extrinsic capacitances for GaN HEMT technology. IEEE Microwave and Wireless Components Letters, 2011, vol. 21, no. 8, p. 445–447. DOI: 10.1109/LMWC.2011.2160525
28. JARNDAL, A., CRUPI, G., ALIM, M. A., et al. Equivalent-circuit extraction for gallium nitride electron devices: Direct versus optimization-empowered approaches. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2022, vol. 35, no. 5, p. 1–11. DOI: 10.1002/jnm.3008
29. SU, H., ZHAO, D., HEIDARI, A. A., et al. RIME: A physics based optimization. Neurocomputing, 2023, vol. 532, p. 183–214. DOI: 10.1016/j.neucom.2023.02.010
30. ZHONG, R., YU, J., ZHANG, C., et al. SRIME: A strengthened RIME with Latin hypercube sampling and embedded distance based selection for engineering optimization problems. Neural Computing and Applications, 2024, vol. 36, no. 12, p. 6721–6740. DOI: 10.1007/s00521-024-09424-4
31. HOCKER, A., KARTVELISHVILI, V. SVD approach to data unfolding. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1996, vol. 372, no. 3, p. 469–481. DOI: 10.1016/01689002(95)01478-0
32. RUBINSTEIN, R., PELEG, T., ELAD, M. Analysis K-SVD: A dictionary-learning algorithm for the analysis sparse model. IEEE Transactions on Signal Processing, 2013, vol. 61, no. 3, p. 661–677. DOI: 10.1109/TSP.2012.2226445
33. ANDREWS, H., PATTERSON, C. Singular Value Decomposition (SVD) image coding. IEEE Transactions on Communications, 1976, vol. 24, no. 4, p. 425–432.
34. DOI: 10.1109/TCOM.1976.1093309
35. ZHU, W., LI, Z., HEIDARI, A. A., et al. An enhanced RIME optimizer with horizontal and vertical crossover for discriminating micro seismic and blasting signals in deep mines. Sensors, 2023, vol. 23, no. 21, p. 1–33. DOI: 10.3390/s23218787
36. LI, Y., ZHAO, D., MA, C., et al. CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images. Computers in Biology and Medicine, 2024, vol. 169, p. 1–56. DOI: 10.1016/j.compbiomed.2023.107838
37. ZHU, W., FANG, L., YE, X., et al. IDRM: Brain tumor image segmentation with boosted RIME optimization. Computers in Biology and Medicine, 2023, vol. 166, p. 1–18. DOI: 10.1016/j.compbiomed.2023.107551
38. OOI, B. L., MA, J. Y. Consistent and reliable MESFET parasitic capacitance extraction method. IEE Proceedings - Microwaves, Antennas and Propagation, 2004, vol. 151, no. 1, p. 81–84. DOI: 10.1049/ip-map:20040128
39. ZHANG, J., WANG, S., LIU, M., et al. An improved GaN PHEMT small-signal equivalent circuit with its parameter extraction. Microelectronics Journal, 2021, vol. 112, p. 1–6. DOI: 10.1016/j.mejo.2021.105042
40. KHUSRO, A., HASHMI, M. S., ANSARI, A. Q., et al. An accurate and simplified small signal parameter extraction method for GaN HEMT. International Journal of Circuit Theory and Applications, 2019, vol. 47, no. 6, p. 941–953. DOI: 10.1002/cta.2622
41. CHEN, G., KUMAR, V., SCHWINDT, R. S., et al. A low gate bias model extraction technique for AlGaN/GaN HEMTs. IEEE Transactions on Microwave Theory and Techniques, 2006, vol. 54, no. 7, p. 2949–2953. DOI: 10.1109/TMTT.2006.877047
42. MAJUMDAR, S., BAG, A., BISWAS, D. Comparative analysis of parameter extraction techniques for AlGaN/GaN HEMT on silicon/sapphire substrate. Microelectronics Reliability, 2017, vol. 78, p. 389–395. DOI: 10.1016/j.microrel.2017.08.016
43. ZHANG, Z. The singular value decomposition, applications and beyond. Oct. 29, 2015. arXiv: arXiv:1510.08532, p. 1–114. DOI: 10.48550/arXiv.1510.08532
44. MEES, A. I., RAPP, P. E., JENNINGS, L. S. Singular-value decomposition and embedding dimension. Physical Review A, 1987, vol. 36, no. 1, p. 340–346. DOI: 10.1103/PhysRevA.36.340
45. DEMIREL, H. ANBARJAFARI, G. JAHROMI, M. N. S. Image equalization based on singular value decomposition. In 2008 23rd International Symposium on Computer and Information Sciences. Istanbul (Turkey), 2008, p. 1–5. DOI:10.1109/ISCIS.2008.4717878

Keywords: GaN HEMT, small-signal model, hybrid approach, Singular Value Decomposition (SVD) algorithm, Rime Optimization algorithm

J. Wang, Y. Liu, C. Li, Z. Wang, Y. Li [references] [full-text] [DOI: 10.13164/re.2025.0660] [Download Citations]
A Two-Stage Optimization Framework for Radar Jamming Effectiveness Evaluation

In complex electromagnetic environments, radar signals intercepted by jammers often contain biased data due to factors such as radar mode switching, electromagnetic interference, and receiver noise. To address this challenge, this paper proposes a two-stage optimization framework for jamming effect evaluation from the jammer’s perspective. In the first stage, a pre-evaluation is conducted using an entropy-optimized K-means discretization algorithm (KDEOA) to adaptively partition pulse descriptor word (PDW) parameters, enhancing robustness against noise. A GCSAO-LSSVM model is then employed to improve classification accuracy through optimal parameter tuning and a periodic oscillation mutation strategy. In the second stage, an improved entropy weight method (IEWM) integrating Tsallis entropy, kernel density standardization, and game theory is used for objective weighting, followed by an enhanced TOPSIS method (ITOPSIS) incorporating interquartile range standardiza-tion and dynamic ideal solution fusion for quantitative scoring. Experimental results demonstrate that the pro-posed framework achieves the highest pre-evaluation accuracy across all noise levels (up to 50% contamination), with IEWM exhibiting the lowest weight variation rate (0.11–0.23%) and ITOPSIS showing the strongest correlation (0.7290) with baseline scores under high noise. The main limitations include sensitivity to severe signal distortion and assumption of stable radar behavior. This approach enables accurate, non-cooperative jamming assessment and supports robust decision-making in cognitive electronic warfare.

1. WANG, Z., LI, T., LIU, J. Radar jamming effect analysis based on Bayesian inference network with adaptive clustering. IEEE Sensors Journal, 2021, vol. 21, no. 13, p. 15153–15160. DOI: 10.1109/JSEN.2021.3072624
2. LIU, S. T., LEI, Z. S., GE, Y. A review on evaluation technology of jamming effects of electronic countermeasure (in Chinese). Journal of China Academy of Electronics and Information Technology, 2020, vol. 15, no. 4, p. 306–317+342. DOI: 10.3969/j.issn.1673-5692.2020.04.003
3. ZHOU, H. An introduction of cognitive electronic warfare system. In Processing of the International Conference on Communications, Signal Processing, and Systems. 2020, vol. 517, p. 1202–1210. DOI: 10.1007/978-981-13-6508-9_144
4. ZHANG, Y., SI, G. Y., WANG, Y. Z. Modelling and simulation of cognitive electronic attack under the condition of system of systems combat (in Chinese). Defense Science Journal, 2020, vol. 70, no. 2, p. 183–189.
5. XIE, D., PAN, Y., ZHAO, Y., et al. Multi-function radar cognitive interference decision-making based on improved DQN. In Processing of IEEE International Conference on Signal, Information and Data Processing. Zhuhai (China), 2024, p. 1-6. DOI: 10.1109/ICSIDP62679.2024.10868900
6. LIU, L., CAO, F. Anti-jamming effectiveness evaluation of radio fuze based on the grey analytic hierarchy process. IOP Conference Series: Earth and Environmental Science, 2019, vol. 252, no. 5, p. 1–12. DOI: 10.1088/1755-1315/252/5/052125
7. LIU, X., YANG, J., HOU, B., et al. Radar seeker performance evaluation based on information fusion method. SN Applied Sciences, 2020, vol. 2, no. 4, p. 1–9. DOI: 10.1007/s42452-0202510-0
8. WANG, X., WANG, X., ZHU, J., et al. A hybrid fuzzy method for performance evaluation of fusion algorithms for integrated navigation system. Aerospace Science and Technology, 2017, vol. 69, no. 5, p. 226–235. DOI: 10.1016/j.ast.2017.06.027
9. YANG, C., WANG, Q., PENG, W., et al. Correlation coefficients of Pythagorean hesitant fuzzy sets and their application to radar LPI performance evaluation. Turkish Journal of Electrical Engineering and Computer Sciences, 2020, vol. 28, no. 2, p. 969 to 983. DOI: 10.3906/elk-1906-177
10. YANG, Y., ZHANG, M., DAI, Y. A fuzzy comprehensive CSSVR model-based health status evaluation of radar. PloS one, 2019, vol. 14, no. 3, p. 1–20. DOI: 10.1371/journal.pone.0213833
11. ZHANG, Y., ZHANG, P., ZHANG, B., et al. Health condition assessment of marine systems based on an improved radar chart. Mathematical Problems in Engineering, 2020, p. 1–12. DOI: 10.1155/2020/8878908
12. ZHANG, S., TAO, Y., ZHAO, Y., et al. Research on radar jamming evaluation method based on BP neural network. In Proceedings of the 2nd International Conference on Electrical and Electronic Engineering. 2019, p. 300–305. DOI: 10.2991/eee-19.2019.48
13. TIAN, T., ZHOU, F., LI, Y., et al. Performance evaluation of deception against synthetic aperture radar based on multi feature fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, vol. 14, p. 103–115. DOI: 10.1109/JSTARS.2020.3028858
14. QIN, F. T., JIE, M., JING, D., et al. Radar jamming effect evaluation based on AdaBoost combined classification model. In IEEE 4th International Conference on Software Engineering and Service Science, Beijing (China), 2013, p. 910–913. DOI: 10.1109/ICSESS.2013.6615453
15. HUANG, R. Research on interference effect evaluation and interference decision methods in electronic warfare (in Chinese). Theses. Harbin Engineering University, 2024.
16. LEI, Z. S., LIU, S. T., CHEN, Q. Online evaluation method of interference effect based on SVM-DS fusion. Journal of Detection Control, 2020, vol. 42, no. 3, p. 92–98.
17. LI, T., WANG, Z., LIU, J. Evaluation method for impact of jamming on radar based on expert knowledge and data mining. IET Radar, Sonar & Navigation, 2020, vol. 14, no. 9, p. 1441–1450. DOI: 10.1049/iet-rsn.2020.0141
18. ZHANG, W., WANG, Y., ZHAO, Z., et al. An online jamming effect evaluation method for asymmetric radar information in complex electromagnetic environment. IEEE Geoscience and Remote Sensing Letters, 2023, vol. 20, p. 1–5. DOI: 10.1109/LGRS.2023.3319343
19. PEI, L. G., MA, C. B., LIU, K. The radar jamming scheme online decision method based on SDAE-SVM algorithm (in Chinese). Microelectronics & Computer, 2024, vol. 41, no. 6, p. 83–89. DOI: 10.19304/J.ISSN1000-7180.2023.0401
20. HU, Z. H., DING, S. H., WANG, X. J. Research into online assistant evaluation technology of jamming effect of active radar countermeasure
21. (in Chinese). Shipboard Electronic Countermeasure, 2024, vol. 47, no. 5, p. 32–37.
22. YANG, T. J., WANG, X., CHENG, C. S., et al. Research on the evaluation method of cooperative jamming effectiveness based on IPSO-ELM. AIP Advances, 2025, vol. 15, p. 1–11. DOI: 10.1063/5.0237796
23. ZOU, X. T., DAI, J. H. Feature selection based on rough diversity entropy. Pattern Recognition, 2026, vol. 170, p. 1–13. DOI: 10.1016/j.patcog.2025.112032
24. PANDEY, V., KOMAL, DINCER, H. A review on TOPSIS method and its extensions for different applications with recent development. Soft Computing, 2023, vol. 27, p. 18011–18039. DOI: 10.1007/ s00500-023-09011-0
25. CHEN, R. Y., ZHANG, Y., LI, X., et al. Hybrid model–data driven radar jamming effectiveness evaluation method for accuracy amprovement. Remote Sensing, 2024, vol. 17, no. 258, p. 1–21. DOI: 10.3390/rs17020258
26. NIAN, P. L. Vague-TOPSIS method and its application in jamming effectiveness evaluation. In IEEE 3rd International Conference on Image Processing and Computer Applications (ICIPCA). Shenyang (China), 2025, p. 1133–1137. DOI: 10.1109/ICIPCA65645.2025.11138937
27. FANG, M. J., CHEN, Z. Jamming effect evaluation of radar based on an improved TOPSIS method (in Chinese). Information Countermeasure Technology, 2023, vol. 2, no. 2, p. 90–96. DOI: 10.12399/j.issn.2097-163x.2023.02.008
28. LEI, Z., LIU, S., GE, Y. Online evaluation index system and method for jamming effect of radar (in Chinese). Journal of Chinese Academy of Electronic Sciences, 2020, vol. 15, no. 07, p. 691–697. DOI: 10.3969/j.issn.1673-5692.2020.07.014
29. CHEN, H. L., YANG, A. L., LIU. R., et al. K-means clustering based maximal residual (block) Kaczmarz methods for solving large scale system of linear equations. Computational and Applied Mathematics, 2025, vol. 44, no. 307, p. 1–16 DOI: 10.1007/s40314-025-03265-0
30. QIN, T., LI, Z., ZHAO, J. Mixed precision quantization based on information entropy. Scientific Reports, 2025, vol. 15, p. 1–16. DOI: 10.1038/s41598-025-91684-8
31. WANG, P., FAN, E., WANG, P. Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recognition Letters, 2020, vol. 141, no. 11, p. 61–67. DOI: 10.1016/j.patrec.2020.07.042
32. DENG, L., LIU, S. Snow ablation optimizer: A novel metaheuristic technique for numerical optimization and engineering design. Expert Systems with Applications, 2023, vol. 225, p. 1–18. DOI: 10.1016/j.eswa.2023.120069
33. GU, C. F., HUANG, M., SONG, X. Y., et al. Kernel density estimation in metric spaces. Scandinavian Journal of Statistics, 2025, vol. 52, no. 2, p. 1018–1057. DOI: 10.1111/sjos.12779
34. BORTOT, S., MARQUES PEREIRA, R. A., STAMATOPOULOU, A. Optimal weights and feasible orness of ordered weighted averaging functions in the framework of Tsallis entropy. Fuzzy Sets and Systems, 2025, vol. 517, p. 1–25. DOI: 10.1016/j.fss.2025.109471
35. PARSONS, S., WOOLDRIDGE, M. Game theoretic and decision theoretic agents. The Knowledge Engineering Review, 2000, vol. 15, no. 2, p. 181–185. DOI: 10.1017/S0269888900001016
36. BAGIROV, A., ALIGULIYEV, R., SULTANOVA, N. Finding compact and well-separated clusters: Clustering using silhouette coefficients. Pattern Recognition, 2022, vol. 135, p. 1–15. DOI: 10.1016/j.patcog.2022.109144
37. ZHANG, W., MA, D., ZHAO, Z., et al. LIU, F., Design of cognitive jamming decision-making system against MFR based on reinforcement learning. IEEE Transactions on Vehicular Technology, 2023, vol. 72, no. 8, p. 10048–10062. DOI: 10.1109/TVT.2023.3261318

Keywords: Non-cooperative dynamic adversarial, jamming effect evaluation, improved entropy weight method (IEWM), improved technique for order preference by similarity to ideal solution (ITOPSIS), biased data, K-means discretization based on entropy optimization algorithm (KDEOA)