Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation

TL;DR

This paper introduces structured turbo compressed sensing algorithms for efficient downlink massive MIMO-OFDM channel estimation, exploiting structured sparsity in the angle-frequency and angle-delay domains to improve convergence speed and accuracy.

Contribution

The paper develops novel STCS-based algorithms that leverage structured sparsity in MIMO-OFDM channels, offering faster convergence and competitive error performance.

Findings

Proposed algorithms outperform existing methods in convergence speed.

Achieve similar or better error performance across various simulations.

Effectively exploit structured sparsity in AFD and ADD domains.

Abstract

Compressed sensing has been employed to reduce the pilot overhead for channel estimation in wireless communication systems. Particularly, structured turbo compressed sensing (STCS) provides a generic framework for structured sparse signal recovery with reduced computational complexity and storage requirement. In this paper, we consider the problem of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel estimation in a frequency division duplexing (FDD) downlink system. By exploiting the structured sparsity in the angle-frequency domain (AFD) and angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the channel by using AFD and ADD probability models and design message-passing based channel estimators under the STCS framework. Several STCS-based algorithms are proposed for massive MIMO-OFDM channel estimation by exploiting the structured sparsity. We show that, compared with other existing algorithms, the proposed algorithms have a much faster convergence speed and achieve competitive error performance under a wide range of simulation settings.