Test-Time Adaptation for Robust Modulation Recognition Under Unknown Channel Distortions

In wireless communication systems, the modulated radio signals are commonly susceptible to sensor-level changes and defects during transmission, resulting in data distribution shifts in the received signals. Deep learning-based automatic modulation recognition (AMR) has made significant strides but still struggles with performance on out-of-distribution (OOD) samples due to domain shifts in practical communication systems. Test-time adaptation (TTA) methods adapt models using test samples at inference time, emerging as a promising solution to this challenge. In this letter, we propose a novel self-supervised TTA strategy to adapt mask autoencoders to better recognize modulation modes in OOD scenarios. The key idea is to optimize the main modulation recognition task and the modulated signal spectrogram reconstruction task during training toward effective feature extraction, and design an entropy control mechanism to adapt the model toward better modulation recognition of the test signals. With extensive experiments, we show that the proposed method effectively improves the performance of TTA for AMR in various distribution shift scenarios.