标题： 在线元学习信道自编码器用于动态端到端物理层优化 显示英文标题

标题： Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization

摘要： 通道自编码器（CAEs）在通过端到端联合训练优化无线通信系统的物理层方面表现出显著的潜力。 然而，CAEs的实际实现面临诸多挑战，尤其是在现实和动态的场景中。 通信系统中的信道是动态变化的。然而，大多数提出的CAE设计假设是静态场景，意味着它们仅针对一个信道实现进行训练和测试，而未考虑无线通信系统的动态特性。 此外，传统的CAEs是基于能够访问大量导频信号的假设设计的，在CAE的背景下，这些导频信号作为训练样本。 然而，在实际应用中，对于实时运行的CAE来说，为每个新的信道实现获取大量训练样本是不可行的。 因此，CAE必须能够在少样本学习场景中部署，其中只有有限的训练样本可用。 此外，大多数提出的传统CAEs对新的信道实现缺乏快速适应能力，当导频数量有限时，这种不足更加明显。 为了解决这些挑战，本文提出了在线元学习通道AE（OML-CAE）框架，用于动态信道下的少样本CAE场景。 OML-CAE框架以在线方式增强对不同信道条件的适应能力，允许根据不断变化的通信场景进行动态调整。 此外，它仅使用少量导频就能适应新的信道条件，大幅提高导频效率，并使CAE设计在现实场景中可行。 显示英文摘要

摘要： 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.