Block-Fading non-Stationary Channel Estimation for MIMO-OFDM Systems via Meta-Learning

Abstract

Deep learning (DL)-based channel estimations for multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have shown remarkable performance at the cost of huge sample size and complexity. While such complexity can be offloaded onto offline training phase for stationary channels, this becomes problematic when non-stationary channels are arisen. In this letter, we resolve this issue by proposing meta-learning-aided online training that only requires small sample size with reduced complexity. Numerical results under 3GPP channel models verify that proposed meta-learning approach outperforms not only conventional DL-based estimators but also conventional model-based estimators, e.g., least squares and linear minimum mean square error estimators, especially in the small sample size/low complexity regime.