ZTE Communications ›› 2025, Vol. 23 ›› Issue (2): 20-30.DOI: 10.12142/ZTECOM.202502004
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AI Bo1, ZHANG Yuxin1, YANG Mi1(), HE Ruisi1, GUO Rongge2
Received:
2025-03-17
Online:
2025-06-25
Published:
2025-06-10
Contact:
YANG Mi
About author:
AI Bo received his MS and PhD degrees from Xidian University, China in 2002 and 2004, respectively. He was an Excellent Postdoctoral Research Fellow with Tsinghua University, China in 2007. He is currently a professor with the State Key Laboratory of Advanced Rail Autonomous Operation and School of Electronic and Information Engineering, Beijing Jiaotong University, China. He has authored or co-authored eight books and published over 300 academic research papers. His five papers have been recognized as ESI highly cited papers. His research interests include channel measurement and channel modeling, and dedicated mobile communications for rail traffic systems.Supported by:
AI Bo, ZHANG Yuxin, YANG Mi, HE Ruisi, GUO Rongge. A Machine Learning-Based Channel Data Enhancement Platform for Digital Twin Channels[J]. ZTE Communications, 2025, 23(2): 20-30.
Parameter | Value |
---|---|
Carrier frequency | 5.9 GHz |
Bandwidth | Max to 160 MHz |
Transmit power | Max to 55 dBm |
Transmit signal type | Multi-carrier signals |
Transmit signal samples | 1 024 |
Snapshot interval | 6.4 |
Table 1 Parameters of measurement subsystem
Parameter | Value |
---|---|
Carrier frequency | 5.9 GHz |
Bandwidth | Max to 160 MHz |
Transmit power | Max to 55 dBm |
Transmit signal type | Multi-carrier signals |
Transmit signal samples | 1 024 |
Snapshot interval | 6.4 |
Figure 6 Algorithm verification results: (a) measured PDP; (b) generated PDP; (c) PDP comparison; (d) path loss;(e) RMS delay spread; (f) BER performance
1 | LU K, HAN B M, ZHOU X S. Smart urban transit systems: from integrated framework to interdisciplinary perspective [J]. Urban rail transit, 2018, 4( 2): 49– 67. DOI: 10.1007/s40864-018-0080-x |
2 | HATA M. Empirical formula for propagation loss in land mobile radio services [J]. IEEE transactions on vehicular technology, 1980, 29( 3): 317– 325. DOI: 10.1109/T-VT.1980.23859 |
3 | ZAJIC A G, STUBER G L, PRATT T G, et al. Wideband MIMO mobile-to-mobile channels: geometry-based statistical modeling with experimental verification [J]. IEEE transactions on vehicular technology, 2009, 58( 2): 517– 534. DOI: 10.1109/TVT.2008.928001 |
4 | SALEH A A M, VALENZUELA R. A statistical model for indoor multipath propagation [J]. IEEE journal on selected areas in communications, 1987, 5( 2): 128– 137. DOI: 10.1109/JSAC.1987.1146527 |
5 | HE R S, AI B, STÜBER G L, et al. Geometrical-based modeling for millimeter-wave MIMO mobile-to-mobile channels [J]. IEEE transactions on vehicular technology, 2018, 67( 4): 2848– 2863. DOI: 10.1109/TVT.2017.2774808 |
6 | PANG L H, ZHANG J, ZHANG Y, et al. Investigation and comparison of 5G channel models: from QuaDRiGa, NYUSIM, and MG5G perspectives [J]. Chinese journal of electronics, 2022, 31( 1): 1– 17. DOI: 10.1049/cje.2021.00.103 |
7 | WEINER M. Use of the Longley-Rice and Johnson-Gierhart tropospheric radio propagation programs: 0.02–20 GHz [J]. IEEE journal on selected areas in communications, 1986, 4( 2): 297– 307. DOI: 10.1109/JSAC.1986.1146313 |
8 | AI B, GUAN K, HE R S, et al. On indoor millimeter wave massive MIMO channels: measurement and simulation [J]. IEEE journal on selected areas in communications, 2017, 35( 7): 1678– 1690. DOI: 10.1109/JSAC.2017.2698780 |
9 | GUPTA A, DU J F, CHIZHIK D, et al. Machine learning-based urban canyon path loss prediction using 28 GHz Manhattan measurements [J]. IEEE transactions on antennas and propagation, 2022, 70( 6): 4096– 4111. DOI: 10.1109/TAP.2022.3152776 |
10 | THRANE J, ARTUSO M, ZIBAR D, et al. Drive test minimization using deep learning with Bayesian approximation [C]//Proc. IEEE 88th Vehicular Technology Conference (VTC-Fall). IEEE, 2018: 1– 5. DOI: 10.1109/VTCFall.2018.8690911 |
11 | BAI L, XU Q, HUANG Z W, et al. An atmospheric data-driven Q-band satellite channel model with feature selection [J]. IEEE transactions on antennas and propagation, 2022, 70( 6): 4002– 4013. DOI: 10.1109/TAP.2021.3137285 |
12 | YANG M, AI B, HE R S, et al. Dynamic V2V channel measurement and modeling at street intersection scenarios [J]. IEEE transactions on antennas and propagation, 2023, 71( 5): 4417– 4432. DOI: 10.1109/TAP.2023.3249101 |
13 | HUANG C, HE R S, AI B, et al. Artificial intelligence enabled radio propagation for communications—part II: scenario identification and channel modeling [J]. IEEE transactions on antennas and propagation, 2022, 70( 6): 3955– 3969. DOI: 10.1109/TAP.2022.3149665 |
14 | WANG C L, AI B, HE R S, et al. Channel path loss prediction using satellite images: a deep learning approach [J]. IEEE transactions on machine learning in communications and networking, 2024, 2: 1357– 1368 |
15 | YANG Y, LI Y, ZHANG W X, et al. Generative-adversarial-network-based wireless channel modeling: challenges and opportunities [J]. IEEE communications magazine, 2019, 57( 3): 22– 27. DOI: 10.1109/MCOM.2019.1800635 |
16 | XIAO H, TIAN W Q, LIU W D, et al. ChannelGAN: deep learning-based channel modeling and generating [J]. IEEE wireless communications letters, 2022, 11( 3): 650– 654. DOI: 10.1109/LWC.2021.3140102 |
17 | LIANG X, LIU Z Y, CHANG H R, et al. Wireless channel data augmentation for artificial intelligence of things in industrial environment using generative adversarial networks [C]//Proc. IEEE 18th International Conference on Industrial Informatics (INDIN). IEEE, 2020, 1: 502– 507. DOI: 10.1109/indin45582.2020.9442206 |
18 | ZHANG D Y, ZHAO J H, YANG L H, et al. Generative adversarial network-based channel estimation in high-speed mobile scenarios [C]//Proc. 13th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2021: 1– 5. DOI: 10.1109/WCSP52459.2021.9613362 |
19 | ZHAO H, LYU Q, LIU Y C, et al. Wireless channel measurements and modeling of LTE broadband system for high-speed railway scenarios [J]. Chinese journal of electronics, 2018, 27( 5): 1092– 1097 |
20 | ZHANG Z Y, HE R S, AI B, et al. A cluster-based statistical channel model for integrated sensing and communication channels [J]. IEEE transactions on wireless communications, 2024, 23( 9): 11597– 11611. DOI: 10.1109/TWC.2024.3383594 |
21 | YANG M, AI B, HE R S, et al. Measurements and cluster-based modeling of vehicle-to-vehicle channels with large vehicle obstructions [J]. IEEE transactions on wireless communications, 2020, 19( 9): 5860– 5874. DOI: 10.1109/TWC.2020.2997808 |
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