Sensing-Assisted Sparse Channel Recovery for Massive Antenna Systems

TL;DR

This paper introduces a sensing-assisted sparse channel recovery method for massive antenna systems, leveraging radar sensing and compressive sensing to improve channel estimation accuracy and reduce training overhead.

Contribution

It proposes a novel approach combining radar sensing with compressive sensing for better channel recovery in massive MIMO systems, which is a new integration in this context.

Findings

Significantly improves achievable rate over DFT-based methods

Reduces training overhead in channel estimation

Enhances recovery accuracy with limited feedback

Abstract

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.