Communication Algorithms via Deep Learning

Hyeji Kim, Yihan Jiang, Ranvir B. Rana, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

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 parametrized by recurrent neural network (RNN) architectures. We show that cre- atively 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 gen- eralizations, 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.

TL;DR: We show that creatively designed and trained RNN architectures can decode well known sequential codes and achieve close to optimal performances.

Keywords: coding theory, recurrent neural network, communication

Code: [ yihanjiang/Sequential-RNN-Decoder](https://github.com/yihanjiang/Sequential-RNN-Decoder) + [ 2 community implementations](https://paperswithcode.com/paper/?openreview=ryazCMbR-)

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:1805.09317/code)