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

TL;DR

This paper presents a proactive beamforming scheme for mmWave V2I communication that uses multi-modal sensing and neural networks to improve beam alignment accuracy, reduce signaling overhead, and enhance system reliability in dynamic environments.

Contribution

It introduces MMFF-Net, a novel multi-modal feature fusion neural network that integrates sensing and communication data for accurate beam prediction in vehicular networks.

Findings

MMFF-Net improves angle prediction accuracy.

The scheme increases achievable data rates.

It reduces communication outage probability.

Abstract

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.