Segment-Wise Flow Matching for Vision-Aided mmWave V2I Beam Prediction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910120136409088 |
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| author | Zheng, Can He, Jiguang Kang, Chung G. Cai, Guofa Huang, Chongwen Wymeersch, Henk |
| author_facet | Zheng, Can He, Jiguang Kang, Chung G. Cai, Guofa Huang, Chongwen Wymeersch, Henk |
| contents | This paper proposes a vision-conditioned flow matching (FM) framework for beam prediction in millimeter-wave vehicle-to-infrastructure links. Instead of modeling discrete beam-index sequences, the proposed method learns the temporal evolution of normalized beam receive power vectors through a continuous vector field governed by an ordinary differential equation, enabling smooth dynamics and efficient sampling. By imposing FM over beam-state transitions and jointly optimizing beam prediction and flow consistency, the proposed framework provides a unified model for future beam prediction. Experimental results show that the proposed FM-based model significantly improves beam prediction performance over baselines, approaches the performance of large language model-based methods, and reduces predictor-side inference latency by about $6.9\times$ on GPU and $2.8\times10^3\times$ on CPU, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Segment-Wise Flow Matching for Vision-Aided mmWave V2I Beam Prediction Zheng, Can He, Jiguang Kang, Chung G. Cai, Guofa Huang, Chongwen Wymeersch, Henk Signal Processing This paper proposes a vision-conditioned flow matching (FM) framework for beam prediction in millimeter-wave vehicle-to-infrastructure links. Instead of modeling discrete beam-index sequences, the proposed method learns the temporal evolution of normalized beam receive power vectors through a continuous vector field governed by an ordinary differential equation, enabling smooth dynamics and efficient sampling. By imposing FM over beam-state transitions and jointly optimizing beam prediction and flow consistency, the proposed framework provides a unified model for future beam prediction. Experimental results show that the proposed FM-based model significantly improves beam prediction performance over baselines, approaches the performance of large language model-based methods, and reduces predictor-side inference latency by about $6.9\times$ on GPU and $2.8\times10^3\times$ on CPU, respectively. |
| title | Segment-Wise Flow Matching for Vision-Aided mmWave V2I Beam Prediction |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.20265 |