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[2602.13606] Multi-Modal Sensing and Fusion in mmWave Beamforming for Connected Vehicles: A Transformer Based Framework
Summary
This article presents a novel multi-modal sensing and fusion framework for mmWave beamforming in connected vehicles, enhancing communication efficiency and reducing overheads.
Why It Matters
As connected vehicles increasingly rely on high-speed communication, optimizing beamforming techniques is crucial. This research addresses significant challenges in dynamic environments, potentially improving vehicle communication systems and paving the way for more efficient transportation technologies.
Key Takeaways
- The proposed framework reduces beam training overheads in vehicular environments.
- Achieves 96.72% accuracy in predicting optimal beams for communication.
- Improves latency and beam searching space overheads by over 86% compared to standard methods.
- Utilizes multi-head cross-modal attention for effective feature fusion.
- Demonstrates generalizability across various vehicle-to-infrastructure and vehicle-to-vehicle scenarios.
Computer Science > Networking and Internet Architecture arXiv:2602.13606 (cs) [Submitted on 14 Feb 2026] Title:Multi-Modal Sensing and Fusion in mmWave Beamforming for Connected Vehicles: A Transformer Based Framework Authors:Muhammad Baqer Mollah, Honggang Wang, Mohammad Ataul Karim, Hua Fang View a PDF of the paper titled Multi-Modal Sensing and Fusion in mmWave Beamforming for Connected Vehicles: A Transformer Based Framework, by Muhammad Baqer Mollah and 3 other authors View PDF HTML (experimental) Abstract:Millimeter wave (mmWave) communication, utilizing beamforming techniques to address the inherent path loss limitation, is considered as one of the key technologies to support ever increasing high throughput and low latency demands of connected vehicles. However, adopting standard defined beamforming approach in highly dynamic vehicular environments often incurs high beam training overheads and reduction in the available airtime for communications, which is mainly due to exchanging pilot signals and exhaustive beam measurements. To this end, we present a multi-modal sensing and fusion learning framework as a potential alternative solution to reduce such overheads. In this framework, we first extract the representative features from the sensing modalities by modality specific encoders, then, utilize multi-head cross-modal attention to learn dependencies and correlations between different modalities, and subsequently fuse the multimodal features to obtain predicted top...
