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.

翻译：深度学习有望促进无线接收器的操作，新兴架构将深度神经网络与传统模块化接收器处理相结合。虽然深度接收器在训练所针对的复杂环境中表现出可靠性能，但无线通信的动态特性要求深度接收器需要不断适应信道变化。然而，频繁的重新训练成本高昂且效果有限，实际上并非每次信道变化都需要调整整个深度神经网络。本文研究概念漂移检测方法，用于识别深度接收器何时不再匹配信道，从而实现异步自适应——仅在必要时进行重新训练。我们识别了机器学习文献中适用于动态信道下深度接收器的现有漂移检测方案，并提出一种专为通信领域设计的软输出检测机制。此外，对于保留传统模块化接收器处理的深度接收器，我们设计了模块化漂移检测机制，能够同时识别重新训练的时机和具体子模块。数值研究表明，即使在快速时变场景中，通过模块化漂移检测实现的异步自适应能显著减少训练参数量和重新训练时间，且性能几乎不受影响。