Channel Autoencoders (CAEs) have shown significant potential in optimizing the physical layer of a wireless communication system for a specific channel through joint end-to-end training. However, the practical implementation of CAEs faces several challenges, particularly in realistic and dynamic scenarios. Channels in communication systems are dynamic and change with time. Still, most proposed CAE designs assume stationary scenarios, meaning they are trained and tested for only one channel realization without regard for the dynamic nature of wireless communication systems. Moreover, conventional CAEs are designed based on the assumption of having access to a large number of pilot signals, which act as training samples in the context of CAEs. However, in real-world applications, it is not feasible for a CAE operating in real-time to acquire large amounts of training samples for each new channel realization. Hence, the CAE has to be deployable in few-shot learning scenarios where only limited training samples are available. Furthermore, most proposed conventional CAEs lack fast adaptability to new channel realizations, which becomes more pronounced when dealing with a limited number of pilots. To address these challenges, this paper proposes the Online Meta Learning channel AE (OML-CAE) framework for few-shot CAE scenarios with dynamic channels. The OML-CAE framework enhances adaptability to varying channel conditions in an online manner, allowing for dynamic adjustments in response to evolving communication scenarios. Moreover, it can adapt to new channel conditions using only a few pilots, drastically increasing pilot efficiency and making the CAE design feasible in realistic scenarios.

翻译：信道自动编码器（CAE）通过联合端到端训练，在针对特定信道优化无线通信系统物理层方面展现出显著潜力。然而，CAE的实际应用面临若干挑战，尤其是在现实动态场景中。通信系统中的信道具有动态性并随时间变化，但现有大多数CAE设计均基于静态场景假设，即仅针对单一信道实现进行训练和测试，未考虑无线通信系统的动态特性。此外，传统CAE的设计基于可获取大量导频信号的假设，这些信号在CAE中充当训练样本。但在实际应用中，实时运行的CAE难以针对每个新信道实现获取大量训练样本。因此，CAE必须能在仅有限训练样本可用的少样本学习场景中部署。更突出的是，多数传统CAE缺乏对新信道实现的快速适应能力，这一问题在处理有限导频时尤为明显。为应对这些挑战，本文提出面向动态信道少样本CAE场景的在线元学习信道自动编码器（OML-CAE）框架。该框架以在线方式增强对时变信道条件的适应能力，支持根据演进中的通信场景进行动态调整。此外，该框架仅需少量导频即可适应新信道条件，显著提升导频效率，使CAE设计在实际场景中具备可行性。