Communication Algorithms via Deep Learning

TL;DR

This paper demonstrates that deep learning, specifically RNN architectures, can automatically discover decoding algorithms for well-known codes, achieving near-optimal performance and generalizing across various conditions.

Contribution

It introduces a method to automate decoding algorithm discovery using deep learning, matching traditional algorithms' performance and showing strong generalization capabilities.

Findings

RNNs can decode convolutional and turbo codes with near-optimal performance.

Deep learning models generalize well across different SNRs and block lengths.

The approach is robust and adaptable to deviations from the AWGN channel.

Abstract

Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parameterized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms). We show strong generalizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.