Disturbance-based Discretization, Differentiable IDS Channel, and an IDS-Correcting Code for DNA Storage

Alan J.X. Guo, Mengyi Wei, Yufan Dai, Yali Wei, Pengchen Zhang

TL;DR: An Autoencoder-based IDS-Correcting Code for DNA Storage

Abstract: Insertion, deletion, and substitution (IDS) error-correcting codes have garnered increased attention with recent advancements in DNA storage technology. However, a universal method for designing tailored IDS-correcting codes across varying channel settings remains underexplored. We present an autoencoder-based approach, THEA-code, aimed at efficiently generating IDS-correcting codes for complex IDS channels. In the work, a disturbance-based discretization is proposed to discretize the features of the autoencoder, and a simulated differentiable IDS channel is developed as a differentiable alternative for IDS operations. These innovations facilitate the successful convergence of the autoencoder, producing channel-customized IDS-correcting codes that demonstrate commendable performance across complex IDS channels, particularly in the realistic DNA storage channel.

Primary Area: Deep Learning->Generative Models and Autoencoders

Keywords: autoencoder, discretization, error-correcting code, IDS channel, DNA storage

Application-Driven Machine Learning: This submission is on Application-Driven Machine Learning.

Submission Number: 513