Emerging recently as a novel concept in communication system design, end-to-end learning introduces deep neural networks (NNs) to represent the transmitter and receiver functions. Consequently, the whole system can be interpreted as an autoencoder (AE), which can be optimized from a holistic approach through a data-driven training method. Until now, the AE technique is mainly developed for point-to-point communication scenarios. In this paper, we aim to develop a novel NN-based AE scheme for relay-assisted cooperative communication systems. Specifically, three NN components are constructed to learn the behavior of the transmitter, relay node, and receiver, respectively. As the conventional end-to-end training is inapplicable, a novel two-stage training approach is proposed to indirectly solve the end-to-end training problem. The implicit approximations involved are analytically expressed based on information theory, offering insights on the achievable performance with the proposed training method. The proposed AE model eliminates the need for channel state information and noise variance of any link, and is adaptive to the variation in the input block length. Simulation results verify its advantages over the conventional decode-and-forward (DF) and amplify-and-forward (AF) schemes in various scenarios.

中文翻译：

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中继辅助协作通信系统的深度自编码器学习

最近作为通信系统设计中的一个新概念出现，端到端学习引入了深度神经网络 (NN) 来表示发射器和接收器功能。因此，整个系统可以被解释为一个自动编码器 (AE)，它可以通过数据驱动的训练方法从整体方法进行优化。到目前为止，AE 技术主要针对点对点通信场景而开发。在本文中，我们旨在为中继辅助协作通信系统开发一种新的基于 NN 的 AE 方案。具体来说，构建了三个 NN 组件来分别学习发射器、中继节点和接收器的行为。由于传统的端到端训练不适用，提出了一种新颖的两阶段训练方法来间接解决端到端的训练问题。所涉及的隐式近似值是基于信息理论的分析表达的，提供了对所提出的训练方法可实现的性能的见解。提出的AE模型不需要任何链路的信道状态信息和噪声方差，并且适应输入块长度的变化。仿真结果验证了其在各种场景中优于传统解码转发 (DF) 和放大转发 (AF) 方案的优势。