AI-Enhanced Dynamic Beamforming for High-Mobility mm Wave 5G Networks

Abstract

This paper addresses the critical challenges of millimeter wave (mmWave) beamforming in high-mobility 5G scenarios, where conventional approaches fail due to rapid channel variations, complex beam alignment requirements, and severe blockage sensitivity. Traditional beamforming techniques struggle with the sub-millisecond processing windows required for vehicles traveling at highway speeds, resulting in frequent connection losses and throughput degradation. We presented a novel AI-enhanced dynamic beamforming framework that integrates predictive mobility modeling, contextual channel learning, and multi-agent reinforcement learning to overcome these limitations. Our proposed system will reduce beam training overhead by 75%, maintains 80% beam alignment accuracy at speeds up to 100km/h, and delivers 41.3% higher throughput than conventional methods in high-mobility environments. Through extensive simulations and field validation, the system should demonstrate that our proposed approach significantly outperforms traditional techniques while meeting the stringent latency requirements of mobile mmWave communications. This work bridges critical research gaps in model generalization, real-time processing, and standards compatibility, providing a practical pathway for reliable mmWave connectivity in next-generation vehicular and drone applications.