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arXiv · 2607.15297

Large Language Model-Enhanced Multi-hop Parallel Image Semantic Communication

Bingyan Xie·Jihong Park·Rui Mao·Longyu Zhou·Tianhao Liang·Yongpeng Wu·Wenjun Zhang
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Abstract

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.

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Bingyan Xie, Jihong Park, Rui Mao, Longyu Zhou, Tianhao Liang, Yongpeng Wu, Wenjun Zhang. 2026-07-02. Large Language Model-Enhanced Multi-hop Parallel Image Semantic Communication. https://arxiv.org/abs/2607.15297

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