Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO

TL;DR

This paper introduces a hybrid channel estimation method for massive MIMO systems that combines compressive sensing and deep unrolling to reduce pilot overhead and maintain high accuracy.

Contribution

It proposes a novel hybrid scheme integrating model-driven CS and data-driven deep unrolling, effectively exploiting channel sparsities for efficient estimation.

Findings

Reduces pilot overhead significantly

Maintains high estimation accuracy

Low complexity compared to existing methods

Abstract

Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intraframe sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity.