Beam Selection

Beam selection in wireless communication, particularly for millimeter-wave systems, aims to efficiently find the optimal transmit and receive antenna configurations for maximizing signal strength and minimizing overhead. Current research heavily utilizes machine learning, employing models like neural networks and decision trees, often incorporating location data (GPS) and multimodal sensor inputs (LiDAR, cameras) to predict optimal beams and reduce the time-consuming search process. This work is crucial for enabling high-speed, reliable communication in dynamic environments like vehicular networks, significantly improving data throughput and reducing latency in applications such as autonomous driving and emergency response.