Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments

TL;DR

This paper introduces a deep residual learning approach for joint pilot design and channel estimation in multi-cell massive MIMO systems, effectively mitigating pilot contamination effects especially under hardware impairments.

Contribution

It proposes a novel transfer learning-based joint pilot design and deep residual channel estimator to improve estimation accuracy in challenging multi-cell massive MIMO environments.

Findings

Outperforms conventional methods in reducing pilot contamination effects.

Enhances channel estimation accuracy using deep residual learning.

Transfer learning further improves performance when prior contamination knowledge is unavailable.

Abstract

In multi-cell massive MIMO systems, channel estimation is deteriorated by pilot contamination and the effects of pilot contamination become more severe due to hardware impairments. In this paper, we propose a joint pilot design and channel estimation based on deep residual learning in order to mitigate the effects of pilot contamination under the consideration of hardware impairments. We first investigate a conventional linear minimum mean square error (LMMSE) based channel estimator to suppress the interference caused by pilot contamination. After that, a deep learning based pilot design is proposed to minimize the mean square error (MSE) of LMMSE channel estimation, which is utilized to the joint pilot design and channel estimator for transfer learning approach. For the channel estimator, we use a deep residual learning which extracts the features of interference caused by pilot contamination and eliminates them to estimate the channel information. Simulation results demonstrate that the proposed joint pilot design and channel estimator outperforms the conventional approach in multi-cell massive MIMO scenarios. Furthermore, the joint pilot design and channel estimator using transfer learning enhances the estimation performance by reducing the effects of pilot contamination when the prior knowledge of pilot contamination cannot be exploited.