Rapid Online Bayesian Learning for Deep Receivers

Abstract

Integrating deep neural networks (DNNs) into wireless receivers can enhance reliability in the presence of hard-to-model channels. However, in order to successfully deploy deep receivers, one must address the rapid channel variations while accounting for the limited availability of data and computing resources. This paper presents a novel framework for rapid online learning of deep receivers that builds on continual Bayesian learning. By modeling the channel variations as a dynamic system in the space of DNN model parameters, we enable efficient single-step updates, supporting the rapid training of Bayesian DNNs using limited data. We propose two online learning algorithms based on extended Kalman filtering and on Bayesian gradients. Unlike typical approaches that avoid catastrophic forgetting, our methods prioritize adapting to current channel realization. Numerical results show that the proposed continual Bayesian learning formulation yields deep receivers that can effectively adapt to varying channels with minimal computational overhead.