DeepBeam: A Multi-Agent Deep Reinforcement Learning Framework for Predictive mmWave Beam Management in Dynamic V2X Networks

Millimeter wave (mmWave) communication in Vehicle-to-Everything (V2X) networks faces significant challenges in beam alignment due to high mobility and dynamic channel conditions. This paper introduces DeepBeam, a novel multi-agent deep reinforcement learning framework that revolutionizes mmWave beam management through three key innovations: (1) a transformer-based trajectory prediction module that achieves 30% lower prediction error than conventional approaches, (2) a privacy-preserving federated learning architecture that enables distributed model training while reducing communication overhead by 70%, and (3) an adaptive message passing mechanism that optimizes multi-agent coordination in real-time. Our comprehensive evaluation, conducted across urban, suburban, and highway scenarios with up to 100 vehicles, demonstrates that DeepBeam maintains over 90% beam alignment accuracy at vehicular speeds up to 120 km/h, while achieving a system throughput of 3.8 Gbps-outperforming state-of-the-art approaches by 40% in accuracy and 35% in throughput. The framework's ability to maintain sub-millisecond latency while ensuring data privacy makes it particularly suitable for deployment in next-generation intelligent transportation systems. Through extensive ablation studies and real-world simulations, we demonstrate that DeepBeam successfully addresses the critical challenges of scalability, efficiency, and reliability in mmWave V2X communications, while accounting for practical implementation constraints through efficient offline learning and hybrid operational strategies.