ChannelMAE: Self-Supervised Learning Assisted Online Adaptation of Neural Channel Estimators

Abstract

A pretrained neural channel estimator cannot generalize to all channel environments, necessitating online adaptation. Conventional methods demand ground-truth channel coefficients as supervised labels, but such labels are unavailable online. To this end, a self-supervised task is introduced on top of the original channel-estimation task to facilitate label-free adaptation of neural estimators. Specifically, this task randomly masks a fraction of resource elements in each received frame and reconstructs such masked parts. To enable effective reconstruction, the task input must incorporate two components: the unmasked parts and estimated data-symbols of masked parts. These estimated symbols are obtained via an online symbol-recovery mechanism, so no additional pilot overhead is incurred. To consolidate the self-supervised task with the original task, a two-branch masked auto-encoder (MAE) model called ChannelMAE is developed, with each branch dedicated to one task. The two branches share the same encoder but use separate decoders. During online adaptation, the encoder is updated by optimizing the self-supervised branch, which learns channel statistical features and shares them with the channel-estimation branch. Therefore, online channel-estimation accuracy is much improved. Extensive experiments show that ChannelMAE reduces channel-estimation error by up to 71.8% and 87.1% compared with the pretrained model and the state-of-the-art adaptation scheme, respectively.