This paper considers the impact of general hardware impairments in a multiple-antenna base station and user equipments on the uplink performance. First, the effective channels are analytically derived for distortion-aware receivers when using finite-sized signal constellations. Next, a deep feedforward neural network is designed and trained to estimate the effective channels. Its performance is compared 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.
@article{arxiv.2208.04033,
title = {Channel Estimation under Hardware Impairments: Bayesian Methods versus Deep Learning},
author = {Özlem Tugfe Demir and Emil Björnson},
journal= {arXiv preprint arXiv:2208.04033},
year = {2022}
}
Comments
Published at the 16th International Symposium on Wireless Communication Systems (ISWCS) 2019, 5 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:1911.07316