Channel Estimation for Millimeter-Wave Massive MIMO with Hybrid Precoding over Frequency-Selective Fading Channels

TL;DR

This paper introduces a novel multi-user uplink channel estimation scheme for millimeter-wave massive MIMO systems over frequency-selective fading channels, leveraging structured sparsity and compressive sensing techniques.

Contribution

It proposes a distributed compressive sensing-based channel estimation method that addresses power leakage and exploits angle-domain sparsity in FSF channels.

Findings

Effective in estimating channels with high accuracy

Reduces power leakage issues in angle estimation

Demonstrates superior performance in simulations

Abstract

Channel estimation for millimeter-wave (mmWave) massive MIMO with hybrid precoding is challenging, since the number of radio frequency (RF) chains is usually much smaller than that of antennas. To date, several channel estimation schemes have been proposed for mmWave massive MIMO over narrow-band channels, while practical mmWave channels exhibit the frequency-selective fading (FSF). To this end, this letter proposes a multi-user uplink channel estimation scheme for mmWave massive MIMO over FSF channels. Specifically, by exploiting the angle-domain structured sparsity of mmWave FSF channels, a distributed compressive sensing (DCS)-based channel estimation scheme is proposed. Moreover, by using the grid matching pursuit strategy with adaptive measurement matrix, the proposed algorithm can solve the power leakage problem caused by the continuous angles of arrival or departure (AoA/AoD). Simulation results verify that the good performance of the proposed solution.