大语言模型增强的多跳并行图像语义通信
Large Language Model-Enhanced Multi-hop Parallel Image Semantic Communication
浏览论文内容
中文总结 AI 辅助
研究提出LLM-MHPSC框架减轻多跳无线图像传输失真累积，设计粗到细残余压缩方案，开发LLM-RTO并提出自适应跳选择策略，实验表明其优于现有方案，为多跳语义通信应用提供灵活有效解决方案。
中文摘要 AI 辅助
本文提出了一种大语言模型增强的多跳并行图像语义通信（LLM-MHPSC）框架，以减轻多跳无线图像传输中的失真累积。与传统单跳语义通信方案不同，LLM-MHPSC在每一跳部署额外的残余补偿链路来抵消累积失真。为最小化额外带宽开销，通过将基于深度学习的压缩器与自适应算术编码（AAC）集成，设计了从粗到细的残余压缩方案。还开发了基于大语言模型的残余传输优化器（LLM-RTO）来准确估计残余分布并实现信道状态和跳感知速率调整，提高不同信道和跳条件下的残余压缩效率。提出了自适应跳选择策略按需激活残余链路，平衡传输性能和计算成本。实验结果表明LLM-MHPSC优于现有语义通信和传统方案，以带宽的少量增加实现了稳健图像传输。该框架为将语义通信扩展到实际多跳应用场景提供了灵活有效的解决方案。
英文摘要
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.
