Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System

ZHONG Weizhi; HE Yi; DUAN Hongtao; WAN Shiqing; FAN Zhenxiong; ZHU Qiuming; LIN Zhipeng

doi:10.11999/JEIT231324

Volume 46 Issue 8

Aug. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(8): 3117-3125

ZHONG Weizhi, HE Yi, DUAN Hongtao, WAN Shiqing, FAN Zhenxiong, ZHU Qiuming, LIN Zhipeng. Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3117-3125. doi: 10.11999/JEIT231324

Citation:

ZHONG Weizhi, HE Yi, DUAN Hongtao, WAN Shiqing, FAN Zhenxiong, ZHU Qiuming, LIN Zhipeng. Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3117-3125. doi: 10.11999/JEIT231324

Citation:

ZHONG Weizhi, HE Yi, DUAN Hongtao, WAN Shiqing, FAN Zhenxiong, ZHU Qiuming, LIN Zhipeng. Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3117-3125. doi: 10.11999/JEIT231324

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Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System

doi: 10.11999/JEIT231324 cstr: 32379.14.JEIT231324

1.
Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
State Radio Monitoring Center, Beijing 100144, China

Funds: The Key Technologies R&D Program of Jiangsu (Prospective and Key Technologies for Industry) (BE2022067, BE2022067-1, BE2022067-3), The National Natural Science Foundation of China (62271250), The Postgraduate Research and Practice Innovation Program of Nanjing University of Aeronautics and Astronautics (xcxjh20231507)

Received Date: 2023-11-30
Rev Recd Date: 2024-06-13

Available Online: 2024-06-21

Publish Date: 2024-08-30

Abstract

Abstract

In order to address the limitations of the joint beamforming method based on channel prior knowledge, which is constrained by multivariate Vehicle-to-Infrastructure (V2I) communication scenes and suffers from large overhead caused by channel estimation, a wireless propagation link prediction-based joint beamforming method assisted by environmental situation awareness is proposed in this paper. Firstly, a model of Reconfigurable Intelligent Surface (RIS) assisted mmWave communication system for V2I networks is established using a ray tracer. To build a dataset, diverse data of wireless propagation links is obtained by changing the environmental situation. Then, this dataset is used to train a machine learning-based wireless propagation link prediction model. Finally, the joint beamforming problem under the constraint of maximum transmission power is modeled. Additionally, based on the prediction outcome, the beamforming matrix of base station and the phase shift matrix of RIS are optimized using Alternating Iterative Optimization Algorithm (AIOA) to maximize the minimum Signal to Interference plus Noise Ratio (SINR) among synchronous communication vehicle users. Simulation results validate the effectiveness of the proposed method. Introducing non-channel prior knowledge driven reduces channel detection overhead and improves feasibility in applying the proposed method to V2I scenes.

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References(21)

References

[1]	CHEN Shanzhi, HU Jinling, SHI Yan, et al. Vehicle-to-everything (V2X) services supported by LTE-based systems and 5G[J]. IEEE Communications Standards Magazine, 2017, 1(2): 70–76. doi: 10.1109/MCOMSTD.2017.1700015.
[2]	WANG Jiadai, LIU Jiajia, and KATO N. Networking and communications in autonomous driving: A survey[J]. IEEE Communications Surveys & Tutorials, 2019, 21(2): 1243–1274. doi: 10.1109/COMST.2018.2888904.
[3]	ZHU Yishi, MAO Bomin, and KATO N. Intelligent reflecting surface in 6G vehicular communications: A survey[J]. IEEE Open Journal of Vehicular Technology, 2022, 3: 266–277. doi: 10.1109/OJVT.2022.3177253.
[4]	LIU Yuanwei, LIU Xiao, MU Xidong, et al. Reconfigurable intelligent surfaces: Principles and opportunities[J]. IEEE Communications Surveys & Tutorials, 2021, 23(3): 1546–1577. doi: 10.1109/COMST.2021.3077737.
[5]	LI Lianlin, RUAN Hengxin, LIU Che, et al. Machine-learning reprogrammable metasurface imager[J]. Nature Communications, 2019, 10(1): 1082. doi: 10.1038/s41467-019-09103-2.
[6]	WU Qingqing and ZHANG Rui. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming[J]. IEEE Transactions on Wireless Communications, 2019, 18(11): 5394–5409. doi: 10.1109/TWC.2019.2936025.
[7]	KONG Xiangping, WANG Yu, ZHANG Lei, et al. Active and passive beamforming for IRS-aided vehicle communication[J]. KSII Transactions on Internet and Information Systems, 2023, 17(5): 1503–1515. doi: 10.3837/TIIS.2023.05.011.
[8]	ZHANG Shuowen and ZHANG Rui. Capacity characterization for intelligent reflecting surface aided MIMO communication[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(8): 1823–1838. doi: 10.1109/JSAC.2020.3000814.
[9]	OMID Y, SHAHABI S M, PAN C H, et al. Low-complexity robust beamforming design for IRS-aided MISO systems with imperfect channels[J]. IEEE Communications Letters, 2021, 25(5): 1697–1701. doi: 10.1109/LCOMM.2021.3049685.
[10]	WANG Jintao, GONG Shiqi, WU Qingqing, et al. RIS-aided MIMO systems with hardware impairments: Robust beamforming design and analysis[J]. IEEE Transactions on Wireless Communications, 2023, 22(10): 6914–6929. doi: 10.1109/TWC.2023.3246990.
[11]	CHEN Yuanbin, WANG Ying, and JIAO Lei. Robust transmission for reconfigurable intelligent surface aided millimeter wave vehicular communications with statistical CSI[J]. IEEE Transactions on Wireless Communications, 2022, 21(2): 928–944. doi: 10.1109/TWC.2021.3100492.
[12]	ALSENWI M, ABOLHASAN M, and LIPMAN J. Intelligent and reliable millimeter wave communications for RIS-aided vehicular networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 21582–21592. doi: 10.1109/TITS.2022.3190101.
[13]	TAHA A, ALRABEIAH M, and ALKHATEEB A. Enabling large intelligent surfaces with compressive sensing and deep learning[J]. IEEE Access, 2021, 9: 44304–44321. doi: 10.1109/ACCESS.2021.3064073.
[14]	LIN Tian, YU Xianghao, ZHU Yu, et al. Channel estimation for IRS-assisted millimeter-wave MIMO systems: Sparsity-inspired approaches[J]. IEEE Transactions on Communications, 2022, 70(6): 4078–4092. doi: 10.1109/TCOMM.2022.3168876.
[15]	PARK J J, LEE J, LIANG Jinyi, et al. Millimeter wave vehicular blockage characteristics based on 28 GHz measurements[C]. 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, Canada, 2017: 1–5. doi: 10.1109/VTCFALL.2017.8287892.
[16]	WANG Yuyang, KLAUTAU A, RIBERO M, et al. MmWave vehicular beam selection with situational awareness using machine learning[J]. IEEE Access, 2019, 7: 87479–87493. doi: 10.1109/ACCESS.2019.2922064.
[17]	朱秋明, 华博宇, 毛开, 等. 无人机毫米波信道建模进展和挑战[J]. 数据采集与处理, 2020, 35(6): 1049–1059. doi: 10.16337/J.1004-9037.2020.06.004. ZHU Qiuming, HUA Boyu, MAO Kai, et al. Advances and challenges of UAV millimeter-wave channel modeling[J]. Journal of Data Acquisition and Processing, 2020, 35(6): 1049–1059. doi: 10.16337/J.1004-9037.2020.06.004.
[18]	朱秋明, 倪浩然, 华博宇, 等. 无人机毫米波信道测量与建模研究综述[J]. 移动通信, 2022, 46(12): 1–11. doi: 10.3969/J.ISSN.1006-1010.20221114-0001. ZHU Qiuming, NI Haoran, HUA Boyu, et al. A survey of UAV millimeter-wave channel measurement and modeling[J]. Mobile Communications, 2022, 46(12): 1–11. doi: 10.3969/J.ISSN.1006-1010.20221114-0001.
[19]	KARIPIDIS E, SIDIROPOULOS N D, and LUO Zhiquan. Quality of service and max-Min fair transmit beamforming to multiple cochannel multicast groups[J]. IEEE Transactions on Signal Processing, 2008, 56(3): 1268–1279. doi: 10.1109/TSP.2007.909010.
[20]	SIDIROPOULOS N D, DAVIDSON T N, and LUO Zhiquan. Transmit beamforming for physical-layer multicasting[J]. IEEE Transactions on Signal Processing, 2006, 54(6): 2239–2251. doi: 10.1109/TSP.2006.872578.
[21]	LUO Zhiquan, MA W K, SO A M C, et al. Semidefinite relaxation of quadratic optimization problems[J]. IEEE Signal Processing Magazine, 2010, 27(3): 20–34. doi: 10.1109/MSP.2010.936019.