Asynchronous Online Adaptation via Modular Drift Detection for Deep Receivers

TL;DR

This paper introduces a modular drift detection approach for deep wireless receivers, enabling targeted, asynchronous adaptation to channel variations, reducing re-training costs while maintaining performance.

Contribution

It proposes novel drift detection mechanisms tailored for deep receivers, including modular detection for sub-module re-training, enhancing adaptation efficiency in dynamic wireless environments.

Findings

Asynchronous adaptation significantly reduces re-training time.

Modular drift detection effectively identifies when and which sub-modules need updating.

Performance remains high despite reduced re-training in time-varying scenarios.

Abstract

Deep learning is envisioned to facilitate the operation of wireless receivers, with emerging architectures integrating deep neural networks (DNNs) with traditional modular receiver processing. While deep receivers were shown to operate reliably in complex settings for which they were trained, the dynamic nature of wireless communications gives rise to the need to repeatedly adapt deep receivers to channel variations. However, frequent re-training is costly and ineffective, while in practice, not every channel variation necessitates adaptation of the entire DNN. In this paper, we study concept drift detection for identifying when does a deep receiver no longer match the channel, enabling asynchronous adaptation, i.e., re-training only when necessary. We identify existing drift detection schemes from the machine learning literature that can be adapted for deep receivers in dynamic channels, and propose a novel soft-output detection mechanism tailored to the communication domain. Moreover, for deep receivers that preserve conventional modular receiver processing, we design modular drift detection mechanisms, that simultaneously identify when and which sub-module to re-train. The provided numerical studies show that even in a rapidly time-varying scenarios, asynchronous adaptation via modular drift detection dramatically reduces the number of trained parameters and re-training times, with little compromise on performance.