Abstract: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.
From: Can Zheng [view email]
[v1]
Tue, 25 Nov 2025 12:50:11 UTC (2,668 KB)
[v2]
Sat, 11 Apr 2026 02:20:26 UTC (2,223 KB)
[v3]
Tue, 25 Aug 2026 17:37:23 UTC (2,113 KB)
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