Channel Estimation under Hardware Impairments: Bayesian Methods versus Deep Learning

• URL: http://arxiv.org/abs/2208.04033v1
• Date: Mon, 8 Aug 2022 10:32:32 GMT
• Title: Channel Estimation under Hardware Impairments: Bayesian Methods versus Deep Learning
• Authors: \"Ozlem Tugfe Demir and Emil Bj\"ornson
• Abstract summary: 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.
• Score: 2.055949720959582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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