AMP-SBL Unfolding for Wideband MmWave Massive MIMO Channel Estimation

TL;DR

This paper introduces an AMP-SBL unfolding method combining deep learning and Bayesian techniques to improve wideband mmWave MIMO channel estimation, addressing beam squint and complexity issues.

Contribution

It proposes a novel AMP-SBL unfolding algorithm that reduces complexity and enhances performance in wideband mmWave MIMO channel estimation.

Findings

Achieves satisfactory estimation performance with low complexity.

Effectively compensates for beam squint effects.

Outperforms traditional CS algorithms in simulations.

Abstract

In wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, channel estimation is challenging due to the hybrid analog-digital architecture, which compresses the received pilot signal and makes channel estimation a compressive sensing (CS) problem. However, existing high-performance CS algorithms usually suffer from high complexity. On the other hand, the beam squint effect caused by huge bandwidth and massive antennas will deteriorate estimation performance. In this paper, frequency-dependent angular dictionaries are first adopted to compensate for beam squint. Then, the expectation-maximization (EM)-based sparse Bayesian learning (SBL) algorithm is enhanced in two aspects, where the E-step in each iteration is implemented by approximate message passing (AMP) to reduce complexity while the M-step is realized by a deep neural network (DNN) to improve performance. In simulation, the proposed AMP-SBL unfolding-based channel estimator achieves satisfactory performance with low complexity.