Multi-hop Parallel Image Semantic Communication for Distortion Accumulation Mitigation

TL;DR

This paper introduces a multi-hop parallel image semantic communication framework with residual compensation and compression techniques to mitigate distortion accumulation over multiple hops, improving transmission robustness.

Contribution

It proposes a novel multi-hop parallel semantic communication scheme with residual compensation and a coarse-to-fine compression method, addressing distortion accumulation in multi-hop wireless image transmission.

Findings

Outperforms existing semantic communication schemes.

Achieves robust multi-hop image transmission with minimal bandwidth increase.

Demonstrates significant reduction in distortion over multiple hops.

Abstract

Existing semantic communication schemes primarily focus on single-hop scenarios, overlooking the challenges of multi-hop wireless image transmission. As semantic communication is inherently lossy, distortion accumulates over multiple hops, leading to significant performance degradation. To address this, we propose the multi-hop parallel image semantic communication (MHPSC) framework, which introduces a parallel residual compensation link at each hop against distortion accumulation. To minimize the associated transmission bandwidth overhead, a coarse-to-fine residual compression scheme is designed. A deep learning-based residual compressor first condenses the residuals, followed by the adaptive arithmetic coding (AAC) for further compression. A residual distribution estimation module predicts the prior distribution for the AAC to achieve fine compression performances. This approach ensures robust multi-hop image transmission with only a minor increase in transmission bandwidth. Experimental results confirm that MHPSC outperforms both existing semantic communication and traditional separated coding schemes.