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The future of vehicular communication networks relies on mmWave massivemulti-input-multi-output antenna arrays for intensive data transfer and massivevehicle access. However, reliable vehicle-to-infrastructure links require exactalignment between the narrow beams, which traditionally involves excessivesignaling overhead. To address this issue, we propose a novel proactivebeamforming scheme that integrates multi-modal sensing and communications viaMulti-Modal Feature Fusion Network (MMFF-Net), which is composed of multipleneural network components with distinct functions. Unlike existing methods thatrely solely on communication processing, our approach obtains comprehensiveenvironmental features to improve beam alignment accuracy. We verify our schemeon the Vision-Wireless (ViWi) dataset, which we enriched with realistic vehicledrifting behavior. Our proposed MMFF-Net achieves more accurate and stableangle prediction, which in turn increases the achievable rates and reduces thecommunication system outage probability. Even in complex dynamic scenarios withadverse environment conditions, robust prediction results can be guaranteed,demonstrating the feasibility and practicality of the proposed proactivebeamforming approach.

Integrated Sensing and Communications Towards Proactive Beamforming in mmWave V2I via Multi-Modal Feature Fusion (MMFF)