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.

提出了一种基于深度学习和变分推断的框架，用于设计具有噪声传输符号的端到端通信系统，同时系统性地集成了通道类型等领域知识，在多个流行通道模型中击败了以前利用深度学习模型的先前工作，实现更好的打包密度和更快的速度。