Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System
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摘要: 为解决基于信道先验知识的联合波束赋形方法受限于多变的车辆与交通基础设施(V2I)通信场景且信道估计开销过大等问题，该文结合环境态势感知，提出一种基于无线传播链路预测的联合波束赋形方法。该方法首先利用射线追踪模拟器构建了可重构智能表面(RIS)辅助的V2I毫米波通信系统模型，通过改变环境态势以获取多样的无线传播链路数据来构建数据集。其次，使用该数据集训练基于机器学习的无线传播链路预测模型。最后，在最大发射功率约束条件下，构建了联合波束赋形问题模型，并基于预测结果采用交替迭代优化方法(AIOA)优化基站波束赋形矩阵和RIS相移矩阵，以实现同步通信车辆用户最小信干噪比(SINR)的最大化。仿真结果验证了该方法的有效性，通过引入非信道先验知识驱动，降低了信道探测开销，提高了该方法在V2I场景中的可行性。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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1 二分搜索算法
初始化参数：$ {\beta _1} $, $ P $, $ \delta $, $ {\boldsymbol{\varTheta}} $,$ {\lambda _{\text{u}}} $, $ {\lambda _{\text{l}}} = 0 $ (1) While $ ({\lambda _{\text{u}}} - {\lambda _{\text{l}}}) \ge {\beta _1} $ do (2) $ t = ({\lambda _{\text{u}}}{\text{ + }}{\lambda _{\text{l}}})/2 $ (3) 利用半定松弛方法解决问题(P2a) (4) if 当前的$ t $和$ {\boldsymbol{\varTheta}} $可以使得问题(P2a)状态为可行 then (5) $ {\lambda _{\text{u}}} = \delta t,{\text{ }}{\lambda _{\text{l}}} = t $ (6) else (7) $ {\lambda _{\text{u}}} = t $ (8) end if (9) end while 输出$ t;{\text{ }}{\boldsymbol{X}}_k^{{\text{opt}}},\forall k = \{ 1,2, \cdots ,K\} $ 
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表 1 通用仿真参数
参数 值 参数 值 M 9 单车道尺寸/(长, 宽)(m) (100, 5) N {16, 36} BS坐标/(水平位置, 高度)(m) (50, 3) L 2 RIS坐标/(水平位置, 高度)(m) (50, 8) K 2 小型车辆尺寸/(长, 宽, 高)(m) (4, 2, 1.5) $ \sigma _k^2 $(dB) –110 大型车辆尺寸/(长, 宽, 高)(m) (9, 2.5, 2.5) $ \kappa $(dB) 4 信号中心频率(GHz) 28
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表 2 不同编码方式对应的环境态势特征下预测模型在测试集上的MSE
T1,1 T2,1 T3,1 T4,1 T5,1 T1,2 T2,2 T3,2 T4,2 T5,2 TVOM 3.07E–07 4.50E–07 2.82E–04 1.71E–02 1.44E–06 7.07E–04 1.24E–03 5.75E–03 2.18E–02 3.25E–03 NOCM 1.86E–07 1.96E–07 9.45E–07 3.96E–05 1.21E–06 3.66E–04 8.60E–04 2.73E–04 4.62E–04 1.55E–03 OLCM,Nv=2 1.22E–07 8.66E–09 7.14E–08 4.04E–07 3.92E–07 2.66E–04 7.75E–04 4.59E–04 5.11E–05 2.13E–03
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[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. -
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