Deep neural network (DNN)-based receivers offer a powerful alternative to classical model-based designs for wireless communication, especially in complex and nonlinear propagation environments. However, their adoption is challenged by the rapid variability of wireless channels, which makes pre-trained static DNN-based receivers ineffective, and by the latency and computational burden of online stochastic gradient descent (SGD)-based learning. In this work, we propose an online learning framework that enables rapid low-complexity adaptation of DNN-based receivers. Our approach is based on two main tenets. First, we cast online learning as Bayesian tracking in parameter space, enabling a single-step adaptation, which deviates from multi-epoch SGD . Second, we focus on modular DNN architectures that enable parallel, online, and localized variational Bayesian updates. Simulations with practical communication channels demonstrate that our proposed online learning framework can maintain a low error rate with markedly reduced update latency and increased robustness to channel dynamics as compared to traditional gradient descent based method.

翻译：基于深度神经网络（DNN）的接收机为无线通信中的经典模型驱动方法提供了强有力的替代方案，尤其在复杂非线性传播环境中优势显著。然而，无线信道的快速时变性导致预训练静态DNN接收机性能失效，而基于在线随机梯度下降（SGD）的学习方法又面临延迟与计算负担过重的挑战，这些因素制约了DNN接收机的实际应用。本研究提出一种在线学习框架，使得DNN接收机能够实现快速低复杂度的自适应。本方法基于两大核心原则：其一，将在线学习重构为参数空间的贝叶斯跟踪过程，实现区别于多轮SGD的单步自适应；其二，聚焦模块化DNN架构，支持并行化、在线且局部化的变分贝叶斯更新。实际通信信道仿真表明，与传统梯度下降方法相比，本文提出的在线学习框架在显著降低更新延迟的同时维持低误码率，并展现出更强的信道动态鲁棒性。