Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning

TL;DR

This paper compares Bayesian and deep learning methods for channel estimation in massive MIMO systems affected by hardware non-linearities, showing deep learning offers superior estimation accuracy especially for higher-order distortions.

Contribution

It introduces deep neural network estimators for effective channels and distortion variance, outperforming traditional Bayesian LMMSE methods under non-linear hardware impairments.

Findings

Deep learning improves channel estimation accuracy over Bayesian methods.

Neural networks effectively estimate higher-order non-linear distortions.

Deep learning-based estimators outperform Bayesian estimators in practical scenarios.

Abstract

This paper considers the joint impact of non-linear hardware impairments at the base station (BS) and user equipments (UEs) on the uplink performance of single-cell massive MIMO (multiple-input multiple-output) in practical Rician fading environments. First, Bussgang decomposition-based effective channels and distortion characteristics are analytically derived and the spectral efficiency (SE) achieved by several receivers are explored for third-order non-linearities. Next, two deep feedforward neural networks are designed and trained to estimate the effective channels and the distortion variance at each BS antenna, which are used in signal detection. We compare the performance of the proposed methods with state-of-the-art distortion-aware and -unaware Bayesian linear minimum mean-squared error (LMMSE) estimators. The proposed deep learning approach improves the estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise. Using the data generated by the derived effective channels for general order of non-linearities at both the BS and UEs, it is shown that the deep learning-based estimator provides better estimates of the effective channels also for non-linearities more than order three.