  The future of vehicular communication networks relies on mmWave massive
multi-input-multi-output antenna arrays for intensive data transfer and massive
vehicle access. However, reliable vehicle-to-infrastructure links require exact
alignment between the narrow beams, which traditionally involves excessive
signaling overhead. To address this issue, we propose a novel proactive
beamforming scheme that integrates multi-modal sensing and communications via
Multi-Modal Feature Fusion Network (MMFF-Net), which is composed of multiple
neural network components with distinct functions. Unlike existing methods that
rely solely on communication processing, our approach obtains comprehensive
environmental features to improve beam alignment accuracy. We verify our scheme
on the Vision-Wireless (ViWi) dataset, which we enriched with realistic vehicle
drifting behavior. Our proposed MMFF-Net achieves more accurate and stable
angle prediction, which in turn increases the achievable rates and reduces the
communication system outage probability. Even in complex dynamic scenarios with
adverse environment conditions, robust prediction results can be guaranteed,
demonstrating the feasibility and practicality of the proposed proactive
beamforming approach.
