Semantic Communication Over Channels With Insertions, Deletions, and Substitutions

Abstract

We present deep joint source-outer channel coding (DeepJSOC), an end-to-end deep learning-based semantic communication architecture designed for channels with insertions, deletions, and substitutions (IDS). We propose a three-stage training algorithm that combines, for the first time, gated recurrent unit (GRU) networks for marker detection, transformer-based semantic communication for continuous latent space, and lookup-free quantization for binarized latent space optimization, specifically tailored to IDS channels. The proposed DeepJSOC is the first to integrate deep learning-based error correction networks into joint-source channel coding schemes for binary channels with synchronization errors. We demonstrate the effectiveness of DeepJSOC by experiments, achieving significant improvements over existing methods in text transmission.