Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912970718576640 |
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| author | Li, Mengyuan Lu, Qianfan Tian, Jiachen Hu, Hongjun Han, Yu Li, Xiao Wen, Chao-kai Jin, Shi |
| author_facet | Li, Mengyuan Lu, Qianfan Tian, Jiachen Hu, Hongjun Han, Yu Li, Xiao Wen, Chao-kai Jin, Shi |
| contents | In near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, spherical wavefront propagation expands the traditional beam codebook into the joint angular-distance domain, rendering conventional beam training prohibitively inefficient, especially in complex 3-dimensional (3D) low-altitude environments. Furthermore, since near-field beam variations are deeply coupled not only with user positions but also with the physical surroundings, precise beam alignment demands profound environmental understanding capabilities. To address this, we propose a large language model (LLM)-driven multimodal framework that fuses historical GPS data, RGB image, LiDAR data, and strategically designed task-specific textual prompts. By utilizing the powerful emergent reasoning and generalization capabilities of the LLM, our approach learns complex spatial dynamics to achieve superior environmental comprehension... |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_16143 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction Li, Mengyuan Lu, Qianfan Tian, Jiachen Hu, Hongjun Han, Yu Li, Xiao Wen, Chao-kai Jin, Shi Signal Processing Artificial Intelligence In near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, spherical wavefront propagation expands the traditional beam codebook into the joint angular-distance domain, rendering conventional beam training prohibitively inefficient, especially in complex 3-dimensional (3D) low-altitude environments. Furthermore, since near-field beam variations are deeply coupled not only with user positions but also with the physical surroundings, precise beam alignment demands profound environmental understanding capabilities. To address this, we propose a large language model (LLM)-driven multimodal framework that fuses historical GPS data, RGB image, LiDAR data, and strategically designed task-specific textual prompts. By utilizing the powerful emergent reasoning and generalization capabilities of the LLM, our approach learns complex spatial dynamics to achieve superior environmental comprehension... |
| title | Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2603.16143 |