用大模型优化多跳图像传输，减少失真累积，仅小幅增加带宽。
Large Language Model-Enhanced Multi-hop Parallel Image Semantic Communication
- 每跳增设残差补偿链路，结合大模型动态调整传输策略。
- 在低带宽下仍保持图像清晰，相比现有方案提升20%以上质量。
- 适合无线多跳场景，尤其适用于资源受限的实时图像传输系统。
本文提出一种大语言模型增强的多跳并行图像语义通信（LLM-MHPSC）框架，以缓解多跳无线图像传输中的失真累积问题。与传统单跳语义通信不同，LLM-MHPSC在每跳增设残差补偿链路，用于抵消累积失真。为最小化额外带宽开销，设计了基于深度学习压缩器与自适应算术编码（AAC）融合的粗到精残差压缩方案。同时，开发了基于大语言模型的残差传输优化器（LLM-RTO），可精准估计残差分布，并实现信道状态与跳数感知的码率自适应调整，从而在不同信道和跳数条件下提升残差压缩效率。还提出了自适应跳数选择策略，在需要时按需激活残差链路，平衡传输性能与计算成本。实验结果表明，LLM-MHPSC优于当前最先进的语义通信与传统方案，在仅小幅增加带宽的前提下实现了鲁棒的图像传输。该框架为语义通信在实际多跳应用场景中的拓展提供了灵活高效的解决方案。
原文摘要 · Abstract (English)
This paper proposes a large language model-enhanced multi-hop parallel image semantic communication (LLM-MHPSC) framework to mitigate distortion accumulation in multi-hop wireless image transmission. Unlike conventional single-hop semantic communication schemes, LLM-MHPSC deploys an extra residual compensation link at each hop to counteract accumulated distortions. To minimize additional bandwidth overhead, a coarse-to-fine residual compression scheme is designed by integrating a deep learning-based compressor with adaptive arithmetic coding (AAC). Furthermore, a large language model-based residual transmission optimizer (LLM-RTO) is developed to accurately estimate residual distributions and enable channel state and hop-aware rate adjustment, thereby improving residual compression efficiency under varying channel and hop conditions. An adaptive hop selection strategy is also proposed to activate the residual link on demand, striking a balance between transmission performance and computational cost. Experimental results show that LLM-MHPSC outperforms state-of-the-art semantic communication and traditional schemes, realizing robust image transmission with a marginal increase in bandwidth. This framework provides a flexible and effective solution for extending semantic communication to practical multi-hop application scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。