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.

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. [ABSTRACT FROM AUTHOR]