Downlink Massive MIMO Channel Estimation via Deep Unrolling : Sparsity Exploitations in Angular Domain

TL;DR

This paper introduces a hybrid deep unrolling framework for downlink massive MIMO channel estimation in FDD systems, reducing pilot overhead and complexity by exploiting angular domain sparsity with minimal prior knowledge.

Contribution

It proposes a novel two-stage hybrid estimation method combining model-driven CS and data-driven deep unrolling, with schemes that require no prior sparsity information.

Findings

Achieves high accuracy with low pilot overhead.

Reduces computational complexity compared to traditional CS methods.

Effective in exploiting intra- and inter-frame sparsity in angular domain.

Abstract

In frequency division duplex (FDD) massive MIMO systems, reliable downlink channel estimation is essential for the subsequent data transmission but is realized at the cost of massive pilot overhead due to hundreds of antennas at base station (BS). In order to reduce the pilot overhead without compromising the estimation, compressive sensing (CS) based methods have been widely applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel in angular domain. However, they still suffer from high complexity during optimization process and the requirement of prior knowledge on sparsity information. To overcome these challenges, this paper develops a novel hybrid channel estimation framework by integrating the model-driven CS and data-driven deep unrolling techniques. The proposed framework is composed of a coarse estimation part and a fine correction part, which is implemented in a two-stage manner to exploit both inter- and intra-frame sparsities of channels in angular domain. Then, two estimation schemes are designed depending on whether priori sparsity information is required, where the second scheme designs a new thresholding function to eliminate such requirement. Numerical results are provided to verify that our schemes can achieve high accuracy with low pilot overhead and low complexity.