Domain Adaptation-Enabled Realistic Map-Based Channel Estimation for MIMO-OFDM

TL;DR

This paper introduces a domain adaptation method for MIMO-OFDM channel estimation that leverages realistic simulations to improve robustness and generalization in dynamic wireless environments.

Contribution

It proposes a novel domain adaptation framework that bridges quasi-static and map-based channel models, enhancing practical channel estimation performance.

Findings

Robust performance with limited true channel data

Effective simulation-based training reduces data needs

Improved generalization across diverse environments

Abstract

Accurate channel estimation is crucial for the improvement of signal processing performance in wireless communications. However, traditional model-based methods frequently experience difficulties in dynamic environments. Similarly, alternative machine-learning approaches typically lack generalization across different datasets due to variations in channel characteristics. To address this issue, in this study, we propose a novel domain adaptation approach to bridge the gap between the quasi-static channel model (QSCM) and the map-based channel model (MBCM). Specifically, we first proposed a channel estimation pipeline that takes into account realistic channel simulation to train our foundation model. Then, we proposed domain adaptation methods to address the estimation problem. Using simulation-based training to reduce data requirements for effective application in practical wireless environments, we find that the proposed strategy enables robust model performance, even with limited true channel information.