Design of Communication Systems using Deep Learning: A Variational Inference Perspective

TL;DR

This paper introduces a variational inference-based framework for designing end-to-end deep learning communication systems that effectively handle noise, outperforming traditional autoencoder approaches in various channel models.

Contribution

It proposes a novel deep neural architecture with a variational inference objective that explicitly accounts for noise in transmit symbols, integrating domain knowledge systematically.

Findings

Models achieve higher packing density.

Faster convergence in training.

Better performance across multiple channel models.

Abstract

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.