Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912193339981824 |
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| author | Cui, Yiming Guo, Jiajia Wen, Chao-Kai Jin, Shi Tong, En |
| author_facet | Cui, Yiming Guo, Jiajia Wen, Chao-Kai Jin, Shi Tong, En |
| contents | Large language models (LLMs) have achieved remarkable success across a wide range of tasks, particularly in natural language processing and computer vision. This success naturally raises an intriguing yet unexplored question: Can LLMs be harnessed to tackle channel state information (CSI) compression and feedback in massive multiple-input multiple-output (MIMO) systems? Efficient CSI feedback is a critical challenge in next-generation wireless communication. In this paper, we pioneer the use of LLMs for CSI compression, introducing a novel framework that leverages the powerful denoising capabilities of LLMs -- capable of error correction in language tasks -- to enhance CSI reconstruction performance. To effectively adapt LLMs to CSI data, we design customized pre-processing, embedding, and post-processing modules tailored to the unique characteristics of wireless signals. Extensive numerical results demonstrate the promising potential of LLMs in CSI feedback, opening up possibilities for this research direction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback Cui, Yiming Guo, Jiajia Wen, Chao-Kai Jin, Shi Tong, En Information Theory Signal Processing Large language models (LLMs) have achieved remarkable success across a wide range of tasks, particularly in natural language processing and computer vision. This success naturally raises an intriguing yet unexplored question: Can LLMs be harnessed to tackle channel state information (CSI) compression and feedback in massive multiple-input multiple-output (MIMO) systems? Efficient CSI feedback is a critical challenge in next-generation wireless communication. In this paper, we pioneer the use of LLMs for CSI compression, introducing a novel framework that leverages the powerful denoising capabilities of LLMs -- capable of error correction in language tasks -- to enhance CSI reconstruction performance. To effectively adapt LLMs to CSI data, we design customized pre-processing, embedding, and post-processing modules tailored to the unique characteristics of wireless signals. Extensive numerical results demonstrate the promising potential of LLMs in CSI feedback, opening up possibilities for this research direction. |
| title | Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2501.10630 |