This correspondence presents a novel sensing-assisted sparse channel recovery approach for massive antenna wireless communication systems. We focus on a fundamental configuration with one massive-antenna base station (BS) and one single-antenna communication user (CU). The wireless channel exhibits sparsity and consists of multiple paths associated with scatterers detectable via radar sensing. Under this setup, the BS first sends downlink pilots to the CU and concurrently receives the echo pilot signals for sensing the surrounding scatterers. Subsequently, the CU sends feedback information on its received pilot signal to the BS. Accordingly, the BS determines the sparse basis based on the sensed scatterers and proceeds to recover the wireless channel, exploiting the feedback information based on advanced compressive sensing (CS) algorithms. Numerical results show that the proposed sensing-assisted approach significantly increases the overall achievable rate than the conventional design relying on a discrete Fourier transform (DFT)-based sparse basis without sensing, thanks to the reduced training overhead and enhanced recovery accuracy with limited feedback.

翻译：本文提出了一种新颖的感知辅助稀疏信道恢复方法，用于大规模天线无线通信系统。我们聚焦于一个基本配置：包含一个大规模天线基站（BS）和一个单天线通信用户（CU）。无线信道表现出稀疏性，并由与可通过雷达感知检测的散射体相关的多条路径组成。在此设置下，基站首先向用户发送下行导频，同时接收回波导频信号以感知周围散射体。随后，用户将其接收到的导频信号反馈信息发送给基站。据此，基站基于感知到的散射体确定稀疏基，并利用基于先进压缩感知（CS）算法的反馈信息恢复无线信道。数值结果表明，与传统依赖离散傅里叶变换（DFT）稀疏基且无感知的设计相比，所提出的感知辅助方法显著提升了总可达速率，这得益于减少的训练开销以及有限反馈下增强的恢复精度。