# Channel Estimation under Hardware Impairments: Bayesian Methods versus Deep Learning

arXiv: https://arxiv.org/abs/2208.04033 · HTML: https://mlsanity.com/paper/2208.04033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 8 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)

## Built on by (most cited first, 1 of 1)

- [Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation](https://mlsanity.com/paper/2608.14676/) (2026)

## Abstract (arXiv)

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.