混合IRS辅助大规模MISO中基于显性信道估计的联合波束成形设计  

Joint beamforming design based on explicit channel estimation in hybrid IRS assisted massive MISO systems

作　　者：李烨[1] 邬婷婷 Li Ye;Wu Tingting(School of Optical-Electrical&Computer Engineering,University of Shanghai for Science&Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《计算机应用研究》2023年第11期3388-3393,共6页Application Research of Computers

基　　金：华为技术有限公司合作项目(YBN2019115054)。

摘　　要：针对智能反射面辅助大规模MISO系统的波束成形设计,当前大多数研究都是基于信道状态信息完全已知,且未考虑基站端主动波束成形矩阵与智能反射面反射系数矩阵存在的耦合关系。智能反射面辅助的信道为级联信道,难以进行估计,且导频开销较大。鉴于此,采用了一种包含有源和无源元件的混合智能反射面架构,首先基于毫米波信道的稀疏特性,利用压缩感知算法估计信道,并设计一种融合注意力机制的两级卷积网络框架,以最大化和速率为目标,联合优化设计智能面反射系数矩阵和波束成形矩阵。实验结果表明,相比已有方法,所提方法可以使用更少的导频获得更优的和速率性能,有效减少了导频损耗,降低了计算时间复杂度。且当通信环境发生变化时,网络亦具有良好的鲁棒性。For the beamforming design of intelligent reflector assisted large-scale MISO systems,most of the current researches are basing on the fully known channel state information,and do not consider the coupling relationship between the active beamforming matrix at the base station and the reflection coefficient matrix of intelligent reflector.The intelligent reflector assisted channel is a cascade channel,which is difficult to estimate and has high pilot frequency overhead.In view of this,this paper proposed a hybrid intelligent reflective surface architecture containing active and passive components.Firstly,based on the sparse characteristics of millimeter-wave channels,it used the compressed sensing algorithm to estimate the channels,and designed a two-stage convolutional network framework integrating attention mechanism to jointly optimize the reflection coefficient matrix and beamforming matrix with the aim of maximizing and rate.The experimental results show that compared to existing methods,the proposed method can achieve better sum rate performance with fewer pilots,effectively reducing pilot loss and reducing computational time complexity.And when the communication environment changes,the network also has good robustness.

关 键 词：大规模MISO 智能反射面 波束成形 信道估计 压缩感知 深度学习 注意力机制 

分 类 号：TP929.5[自动化与计算机技术]