Neural Polar Decoders for DNA Data Storage
Synchronization errors, arising from both synthesis and sequencing noise, present a fundamental challenge in DNA-based data storage systems. These errors are often modeled as insertion-deletion-substitution (IDS) channels, for which maximum-likelihood decoding is quite computationally expensive. In this work, we propose a data-driven approach based on neural polar decoders (NPDs) to design decoders with reduced complexity for channels with synchronization errors.