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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.14532 |
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| _version_ | 1866913898763911168 |
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| author | Zheng, Can He, Jiguang Kang, Chung G. Cai, Guofa Yu, Zitong Debbah, Merouane |
| author_facet | Zheng, Can He, Jiguang Kang, Chung G. Cai, Guofa Yu, Zitong Debbah, Merouane |
| contents | This paper introduces a novel neural network framework called M2BeamLLM for beam prediction in millimeter-wave (mmWave) massive multi-input multi-output (mMIMO) communication systems. M2BeamLLM integrates multi-modal sensor data, including images, radar, LiDAR, and GPS, leveraging the powerful reasoning capabilities of large language models (LLMs) such as GPT-2 for beam prediction. By combining sensing data encoding, multimodal alignment and fusion, and supervised fine-tuning (SFT), M2BeamLLM achieves significantly higher beam prediction accuracy and robustness, demonstrably outperforming traditional deep learning (DL) models in both standard and few-shot scenarios. Furthermore, its prediction performance consistently improves with increased diversity in sensing modalities. Our study provides an efficient and intelligent beam prediction solution for vehicle-to-infrastructure (V2I) mmWave communication systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14532 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models Zheng, Can He, Jiguang Kang, Chung G. Cai, Guofa Yu, Zitong Debbah, Merouane Computation and Language This paper introduces a novel neural network framework called M2BeamLLM for beam prediction in millimeter-wave (mmWave) massive multi-input multi-output (mMIMO) communication systems. M2BeamLLM integrates multi-modal sensor data, including images, radar, LiDAR, and GPS, leveraging the powerful reasoning capabilities of large language models (LLMs) such as GPT-2 for beam prediction. By combining sensing data encoding, multimodal alignment and fusion, and supervised fine-tuning (SFT), M2BeamLLM achieves significantly higher beam prediction accuracy and robustness, demonstrably outperforming traditional deep learning (DL) models in both standard and few-shot scenarios. Furthermore, its prediction performance consistently improves with increased diversity in sensing modalities. Our study provides an efficient and intelligent beam prediction solution for vehicle-to-infrastructure (V2I) mmWave communication systems. |
| title | M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.14532 |