利用无监督学习的RIS辅助毫米波通信系统鲁棒传输设计

Robust Transmission Design for RIS-assisted mmWave Communication Systems Exploiting Unsupervised Learning

本文研究了利用无监督学习的可重构智能表面(reconfigurable intelligent surface,RIS)辅助毫米波大规模多输入多输出(multiple input multiple output,MIMO)下行传输设计。首先,针对发送端信道状态信息(channel state information,CSI)非理想场景,推导了RIS辅助的毫米波下行传输系统平均频谱效率闭式上界。进一步,考虑实际系统硬件受限的条件,本文采用基于离散傅里叶变换(discrete Fourier transform,DFT)码本的模拟预编码,且RIS各反射单元仅能取有限的离散相移值。在此基础上,以所推导的平均频谱效率上界最大化为目标,提出一种基于无监督学习的发送端混合预编码、接收端数字合并以及RIS反射单元相移联合设计方法。所提方法采用两阶段无监督学习模型,分别生成RIS反射单元相移与发送端混合预编码,而接收端数字合并矩阵则采用最小均方误差(minimum mean squared error,MMSE)准则生成。同时,本文针对该模型提出了一种高效的分段训练方法。该训练方法分别对生成RIS反射单元相移与发送端混合预编码的网络进行训练,再对两个网络进行联合训练。仿真结果表明本文所提方法相对传统迭代算法对于非理想CSI具有更好的鲁棒性,当信道估计误差程度增加,本文所提方法性能仅下降约1%,而传统迭代算法则下降约40%。同时,本文所提方法对传输环境变化具有更好的鲁棒性,相对于传统迭代算法能保持至少30%的性能提升。此外,在计算时间开销上本文所提方法相对传统迭代算法有百倍的提升。

This paper investigates the downlink transmission design of a reconfigurable intelligent surface(RIS)-aided millimeter wave massive multiple input multiple output(MIMO)communication system based on unsupervised learning.First,considering imperfect channel state information(CSI)at the transmitter,we derive a closed-form upper bound for the average spectral efficiency of the RIS-aided mmWave downlink transmission system.Furthermore,taking into account the impact of hardware impairment,discrete Fourier transform(DFT)codebook-based hybrid precoding is adopted at the transmitter,and each element of the RIS can only take limited discrete phase shift values.With these conditions,we propose an unsupervised learning based method to jointly design the hybrid precoder at the base station,the phase shift of each element on the RIS,and the digital combiner at the user equipment,under the goal of maximizing the derived system average spectral efficiency upper bound.This method takes a novel two-stage network model to generate the phase shift of each element on the RIS and the hybrid precoder at the base station respectively,while the digital combiner at the user equipment is generated by minimum mean squared error(MMSE)metric.An efficient phased training approach is also developed for the proposed two-stage network model.In this approach,the two networks generating the phase shift of each element on the RIS and the hybrid precoder at the base station are trained separately at the first two stages.After that,the two networks are trained jointly at the third stage.Simulation results demonstrate that the proposed method is more robust to imperfect CSI than the traditional iterative algorithms.When the degree of channel estimation error increases,the performance of the proposed method decreases by only about 1%,while the traditional iterative algorithm decreases by about 40%.At the same time,the proposed method is more robust to the transmission environment,and can maintain a performance improvement of at least 30%compared with traditional iterative algorithms.In addition,the proposed method has a hundred-fold improvement over the traditional iterative algorithm in the computation time cost.