Deep Learning-Based Decoding for Constrained Sequence Codes

TL;DR

This paper introduces deep learning-based decoding methods for constrained sequence codes, significantly improving error rates and throughput, and enabling practical implementation of complex capacity-achieving codes.

Contribution

It presents novel deep learning models for decoding constrained sequence codes, surpassing traditional methods in accuracy and efficiency, and making complex codes practically feasible.

Findings

Deep learning decoders achieve near-MAP error performance.

The approach improves system throughput significantly.

Enables practical implementation of complex capacity-achieving codes.

Abstract

Constrained sequence codes have been widely used in modern communication and data storage systems. Sequences encoded with constrained sequence codes satisfy constraints imposed by the physical channel, hence enabling efficient and reliable transmission of coded symbols. Traditional encoding and decoding of constrained sequence codes rely on table look-up, which is prone to errors that occur during transmission. In this paper, we introduce constrained sequence decoding based on deep learning. With multiple layer perception (MLP) networks and convolutional neural networks (CNNs), we are able to achieve low bit error rates that are close to maximum a posteriori probability (MAP) decoding as well as improve the system throughput. Moreover, implementation of capacity-achieving fixed-length codes, where the complexity is prohibitively high with table look-up decoding, becomes practical with deep learning-based decoding.