Enhancing V2X Communication: Machine Learning Assisted Dynamic mmWave Beam Search

This paper addresses the challenges of dynamic beam search in millimeter wave (mmWave) communications for vehicle-to-everything (V2X) applications. With the rapid mobility of connected autonomous vehicles (CAVs) and dense urban environments, maintaining high-quality mmWave con-nections is critical for the reliability and efficiency of V2X communications. We propose a novel machine learning-assisted framework for dynamic mmWave beam search, which signif-icantly enhances the adaptability and performance of V2X communication systems. Our approach leverages real-time environmental data and CAV dynamics to predict optimal beam directions, improving connection stability. Simulation results demonstrate the effectiveness of the proposed method in a real-world road scenario, offering a partial improvement over conventional beam search techniques.