Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ

TL;DR

This paper introduces a hybrid deep semantic communication system with HARQ that adaptively transmits sentences, significantly reducing bits and errors, and incorporates a similarity detection network to improve resource efficiency.

Contribution

It proposes a novel end-to-end semantic transmission architecture with HARQ, combining semantic coding, traditional channel coding, and similarity detection for improved performance.

Findings

SCHARQ reduces bits and sentence error rate significantly.

SC-RS-HARQ effectively combines semantic and traditional coding.

Similarity detection preserves semantic content with fewer transmissions.

Abstract

Recently, semantic communication has been brought to the forefront because of its great success in deep learning (DL), especially Transformer. Even if semantic communication has been successfully applied in the sentence transmission to reduce semantic errors, existing architecture is usually fixed in the codeword length and is inefficient and inflexible for the varying sentence length. In this paper, we exploit hybrid automatic repeat request (HARQ) to reduce semantic transmission error further. We first combine semantic coding (SC) with Reed Solomon (RS) channel coding and HARQ, called SC-RS-HARQ, which exploits the superiority of the SC and the reliability of the conventional methods successfully. Although the SC-RS-HARQ is easily applied in the existing HARQ systems, we also develop an end-to-end architecture, called SCHARQ, to pursue the performance further. Numerical results demonstrate that SCHARQ significantly reduces the required number of bits for sentence semantic transmission and sentence error rate. Finally, we attempt to replace error detection from cyclic redundancy check to a similarity detection network called Sim32 to allow the receiver to reserve the wrong sentences with similar semantic information and to save transmission resources.