多跳图像语义通信通过并行补偿缓解失真累积，提升传输鲁棒性。
Multi-hop Parallel Image Semantic Communication for Distortion Accumulation Mitigation
- 每跳引入并行残差补偿链路，对抗失真积累
- 粗到精残差压缩降低带宽开销，仅小幅增加传输量
- 适合高跳数、低容错的无线图像传输场景
现有语义通信方案主要聚焦单跳场景，忽视了多跳无线图像传输中的挑战。由于语义通信本质为有损传输，失真在多跳间累积，导致性能显著下降。为此，我们提出多跳并行图像语义通信（MHPSC）框架，在每跳引入并行残差补偿链路以抑制失真累积。为最小化传输带宽开销，设计了粗到精残差压缩方案：先由基于深度学习的残差压缩器对残差进行压缩，再通过自适应算术编码（AAC）实现进一步压缩。同时，残差分布估计模块预测先验分布，使AAC获得更优压缩性能。实验结果表明，该方法在仅小幅增加传输带宽的前提下，显著优于现有语义通信与传统分离编码方案。
原文摘要 · Abstract (English)
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.
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