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.

翻译：现有的语义通信方案主要聚焦于单跳场景，忽视了多跳无线图像传输所面临的挑战。由于语义通信本质上是存在失真的，失真会在多跳传输过程中累积，导致显著的性能下降。为解决此问题，我们提出了多跳并行图像语义通信（MHPSC）框架，该框架在每一跳引入并行残差补偿链路以对抗失真累积。为最小化相关的传输带宽开销，设计了一种由粗到精的残差压缩方案。首先，基于深度学习的残差压缩器对残差进行压缩；随后，采用自适应算术编码（AAC）进行进一步压缩。一个残差分布估计模块预测先验分布以供AAC使用，以实现精细的压缩性能。该方法确保了鲁棒的多跳图像传输，同时仅带来传输带宽的轻微增加。实验结果证实，MHPSC在性能上优于现有的语义通信方案和传统的分离编码方案。