Recent research in the design of end to end communication system using deep learning has produced models which can outperform traditional communication schemes. Most of these architectures leveraged autoencoders to design the encoder at the transmitter and decoder at the receiver and train them jointly by modeling transmit symbols as latent codes from the encoder. However, in communication systems, the receiver has to work with noise corrupted versions of transmit symbols. Traditional autoencoders are not designed to work with latent codes corrupted with noise. In this work, we provide a framework to design end to end communication systems which accounts for the existence of noise corrupted transmit symbols. The proposed method uses deep neural architecture. An objective function for optimizing these models is derived based on the concepts of variational inference. Further, domain knowledge such as channel type can be systematically integrated into the objective. Through numerical simulation, the proposed method is shown to consistently produce models with better packing density and achieving it faster in multiple popular channel models as compared to the previous works leveraging deep learning models.

中文翻译：

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使用深度学习设计通信系统：变分推理视角

最近使用深度学习设计端到端通信系统的研究已经产生了可以超越传统通信方案的模型。这些架构中的大多数都利用自动编码器来设计发射器的编码器和接收器的解码器，并通过将发射符号建模为来自编码器的潜在代码来联合训练它们。然而，在通信系统中，接收器必须使用传输符号的噪声破坏版本。传统的自动编码器并非设计用于处理被噪声破坏的潜在代码。在这项工作中，我们提供了一个框架来设计端到端通信系统，该系统考虑了噪声破坏传输符号的存在。所提出的方法使用深度神经架构。基于变分推理的概念推导出用于优化这些模型的目标函数。此外，诸如频道类型之类的领域知识可以系统地集成到目标中。通过数值模拟，与之前利用深度学习模型的工作相比，所提出的方法显示出一致地生成具有更好填充密度的模型，并在多个流行的通道模型中更快地实现。