Large Language Model-Driven Distributed Integrated Multimodal Sensing and Semantic Communications

• URL: http://arxiv.org/abs/2505.18194v2
• Date: Fri, 30 May 2025 08:40:41 GMT
• Title: Large Language Model-Driven Distributed Integrated Multimodal Sensing and Semantic Communications
• Authors: Yubo Peng, Luping Xiang, Bingxin Zhang, Kun Yang,
• Abstract summary: We propose a novel large language model (LLM)-driven distributed integrated multimodal sensing and semantic communication framework.<n>Specifically, our system consists of multiple collaborative sensing devices equipped with RF and camera modules.<n> evaluations on a synthetic multi-view RF-visual dataset generated by the Genesis simulation engine show that LLM-DiSAC achieves a good performance.
• Score: 5.646293779615063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional single-modal sensing systems-based solely on either radio frequency (RF) or visual data-struggle to cope with the demands of complex and dynamic environments. Furthermore, single-device systems are constrained by limited perspectives and insufficient spatial coverage, which impairs their effectiveness in urban or non-line-of-sight scenarios. To overcome these challenges, we propose a novel large language model (LLM)-driven distributed integrated multimodal sensing and semantic communication (LLM-DiSAC) framework. Specifically, our system consists of multiple collaborative sensing devices equipped with RF and camera modules, working together with an aggregation center to enhance sensing accuracy. First, on sensing devices, LLM-DiSAC develops an RF-vision fusion network (RVFN), which employs specialized feature extractors for RF and visual data, followed by a cross-attention module for effective multimodal integration. Second, a LLM-based semantic transmission network (LSTN) is proposed to enhance communication efficiency, where the LLM-based decoder leverages known channel parameters, such as transceiver distance and signal-to-noise ratio (SNR), to mitigate semantic distortion. Third, at the aggregation center, a transformer-based aggregation model (TRAM) with an adaptive aggregation attention mechanism is developed to fuse distributed features and enhance sensing accuracy. To preserve data privacy, a two-stage distributed learning strategy is introduced, allowing local model training at the device level and centralized aggregation model training using intermediate features. Finally, evaluations on a synthetic multi-view RF-visual dataset generated by the Genesis simulation engine show that LLM-DiSAC achieves a good performance.

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