Model-based learning for joint channel estimationand hybrid MIMO precoding

TL;DR

This paper introduces a lightweight, model-based neural network for joint channel estimation and hybrid precoding in massive MIMO systems, effectively handling hardware impairments and reducing complexity.

Contribution

It proposes an end-to-end neural network architecture that combines unfolded algorithms for channel estimation and precoding, improving robustness and interpretability over traditional methods.

Findings

Demonstrates effective joint channel estimation and precoding on synthetic channels.

Shows robustness to hardware impairments.

Achieves competitive performance with fewer parameters.

Abstract

Hybrid precoding is a key ingredient of cost-effective massive multiple-input multiple-output transceivers. However, setting jointly digital and analog precoders to optimally serve multiple users is a difficult optimization problem. Moreover, it relies heavily on precise knowledge of the channels, which is difficult to obtain, especially when considering realistic systems comprising hardware impairments. In this paper, a joint channel estimation and hybrid precoding method is proposed, which consists in an end-to-end architecture taking received pilots as inputs and outputting pre-coders. The resulting neural network is fully model-based, making it lightweight and interpretable with very few learnable parameters. The channel estimation step is performed using the unfolded matching pursuit algorithm, accounting for imperfect knowledge of the antenna system, while the precoding step is done via unfolded projected gradient ascent. The great potential of the proposed method is empirically demonstrated on realistic synthetic channels.