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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