Alleviating Distortion Accumulation in Multi-Hop Semantic Communication

TL;DR

This paper introduces a recursive training framework for multi-hop semantic communication systems to effectively reduce image distortion accumulation over multiple transmissions, enhancing end-to-end image transmission quality.

Contribution

It proposes a novel recursive training method for semantic encoders and decoders to mitigate distortion accumulation in multi-hop scenarios.

Findings

Significantly reduces image distortion in multi-hop transmissions

Improves robustness of semantic communication systems

Enhances end-to-end image transmission quality

Abstract

Recently, semantic communication has been investigated to boost the performance of end-to-end image transmission systems. However, existing semantic approaches are generally based on deep learning and belong to lossy transmission. Consequently, as the receiver continues to transmit received images to another device, the distortion of images accumulates with each transmission. Unfortunately, most recent advances overlook this issue and only consider single-hop scenarios, where images are transmitted only once from a transmitter to a receiver. In this letter, we propose a novel framework of a multi-hop semantic communication system. To address the problem of distortion accumulation, we introduce a novel recursive training method for the encoder and decoder of semantic communication systems. Specifically, the received images are recursively input into the encoder and decoder to retrain the semantic communication system. This empowers the system to handle distorted received images and achieve higher performance. Our extensive simulation results demonstrate that the proposed methods significantly alleviate distortion accumulation in multi-hop semantic communication.