Continual Learning-Based MIMO Channel Estimation: A Benchmarking Study

TL;DR

This paper evaluates continual learning methods for MIMO channel estimation, addressing non-stationarity issues by testing across varying channel parameters, and demonstrates improved performance over traditional static models.

Contribution

It introduces a benchmarking framework for continual learning in MIMO channel estimation and highlights the importance of addressing catastrophic forgetting in dynamic wireless environments.

Findings

CL methods reduce MSE in changing channel conditions

Catastrophic forgetting impacts continual learning performance

Performance improves with CL in SNR and coherence time variations

Abstract

With the proliferation of deep learning techniques for wireless communication, several works have adopted learning-based approaches to solve the channel estimation problem. While these methods are usually promoted for their computational efficiency at inference time, their use is restricted to specific stationary training settings in terms of communication system parameters, e.g., signal-to-noise ratio (SNR) and coherence time. Therefore, the performance of these learning-based solutions will degrade when the models are tested on different settings than the ones used for training. This motivates our work in which we investigate continual supervised learning (CL) to mitigate the shortcomings of the current approaches. In particular, we design a set of channel estimation tasks wherein we vary different parameters of the channel model. We focus on Gauss-Markov Rayleigh fading channel estimation to assess the impact of non-stationarity on performance in terms of the mean square error (MSE) criterion. We study a selection of state-of-the-art CL methods and we showcase empirically the importance of catastrophic forgetting in continuously evolving channel settings. Our results demonstrate that the CL algorithms can improve the interference performance in two channel estimation tasks governed by changes in the SNR level and coherence time.