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

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.

14 pages, 10 figures, to appear in IEEE Open Journal of the Communications Society

References in corpus (4)

• Learning the MMSE Channel Estimator
• Rate-Splitting to Mitigate Residual Transceiver Hardware Impairments in Massive MIMO Systems
• Hardware Distortion Correlation Has Negligible Impact on UL Massive MIMO Spectral Efficiency
• Channel Estimation under Hardware Impairments: Bayesian Methods versus Deep Learning