Channel Estimation under Hardware Impairments: Bayesian Methods versus Deep Learning
arXiv:2208.04033 · doi:10.1109/ISWCS.2019.8877221
Abstract
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.
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