Recent years have witnessed growing interest in the application of deep neural networks (DNNs) for receiver design, which can potentially be applied in complex environments without relying on knowledge of the channel model. However, the dynamic nature of communication channels often leads to rapid distribution shifts, which may require periodically retraining. This paper formulates a data-efficient two-stage training method that facilitates rapid online adaptation. Our training mechanism uses a predictive meta-learning scheme to train rapidly from data corresponding to both current and past channel realizations. Our method is applicable to any deep neural network (DNN)-based receiver, and does not require transmission of new pilot data for training. To illustrate the proposed approach, we study DNN-aided receivers that utilize an interpretable model-based architecture, and introduce a modular training strategy based on predictive meta-learning. We demonstrate our techniques in simulations on a synthetic linear channel, a synthetic non-linear channel, and a COST 2100 channel. Our results demonstrate that the proposed online training scheme allows receivers to outperform previous techniques based on self-supervision and joint-learning by a margin of up to 2.5 dB in coded bit error rate in rapidly-varying scenarios.

翻译：近年来，深度神经网络在接收机设计中的应用引起了广泛关注，这种技术有望在复杂环境中无需依赖信道模型知识即可运行。然而，通信信道的动态特性常导致快速分布偏移，可能需要周期性重训练。本文提出了一种数据高效的两阶段训练方法，能够支持快速在线自适应。我们的训练机制采用预测性元学习方案，利用与当前和过去信道实现相对应的数据进行快速训练。该方法适用于任何基于深度神经网络的接收机，且无需发送新的导频数据进行训练。为阐述所提方法，我们研究了采用可解释模型驱动架构的深度神经网络辅助接收机，并引入了一种基于预测性元学习的模块化训练策略。我们在合成线性信道、合成非线性信道以及COST 2100信道上通过仿真验证了相关技术。结果表明，所提出的在线训练方案能在快速变化场景中使接收机在编码误码率上显著优于基于自监督和联合学习的现有技术，性能提升幅度可达2.5 dB。