Sensing-Assisted Channel Prediction in Complex Wireless Environments: An LLM-Based Approach
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915287115235328 |
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| author | He, Junjie Ren, Zixiang Yao, Jianping Hu, Han Han, Tony Xiao Xu, Jie |
| author_facet | He, Junjie Ren, Zixiang Yao, Jianping Hu, Han Han, Tony Xiao Xu, Jie |
| contents | This letter studies the sensing-assisted channel prediction for a multi-antenna orthogonal frequency division multiplexing (OFDM) system operating in realistic and complex wireless environments. In this system,an integrated sensing and communication (ISAC) transmitter leverages the mono-static sensing capability to facilitate the prediction of its bi-static communication channel, by exploiting the fact that the sensing and communication channels share the same physical environment involving shared scatterers. Specifically, we propose a novel large language model (LLM)-based channel prediction approach,which adapts pre-trained text-based LLM to handle the complex-matrix-form channel state information (CSI) data. This approach utilizes the LLM's strong ability to capture the intricate spatiotemporal relationships between the multi-path sensing and communication channels, and thus efficiently predicts upcoming communication CSI based on historical communication and sensing CSI data. Experimental results show that the proposed LLM-based approach significantly outperforms conventional deep learning-based methods and the benchmark scheme without sensing assistance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sensing-Assisted Channel Prediction in Complex Wireless Environments: An LLM-Based Approach He, Junjie Ren, Zixiang Yao, Jianping Hu, Han Han, Tony Xiao Xu, Jie Signal Processing This letter studies the sensing-assisted channel prediction for a multi-antenna orthogonal frequency division multiplexing (OFDM) system operating in realistic and complex wireless environments. In this system,an integrated sensing and communication (ISAC) transmitter leverages the mono-static sensing capability to facilitate the prediction of its bi-static communication channel, by exploiting the fact that the sensing and communication channels share the same physical environment involving shared scatterers. Specifically, we propose a novel large language model (LLM)-based channel prediction approach,which adapts pre-trained text-based LLM to handle the complex-matrix-form channel state information (CSI) data. This approach utilizes the LLM's strong ability to capture the intricate spatiotemporal relationships between the multi-path sensing and communication channels, and thus efficiently predicts upcoming communication CSI based on historical communication and sensing CSI data. Experimental results show that the proposed LLM-based approach significantly outperforms conventional deep learning-based methods and the benchmark scheme without sensing assistance. |
| title | Sensing-Assisted Channel Prediction in Complex Wireless Environments: An LLM-Based Approach |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2505.09141 |