Beam Prediction

Beam prediction focuses on accurately forecasting the optimal beam parameters (e.g., direction, power) in various systems, primarily to minimize overhead and improve efficiency in wireless communication and particle accelerators. Current research heavily utilizes deep learning, employing architectures like transformers, recurrent neural networks (RNNs), and large language models (LLMs), often incorporating multimodal data (e.g., camera images, GPS, radar) to enhance prediction accuracy and robustness. These advancements have significant implications for improving the performance and reliability of 5G/6G networks, high-speed rail communications, and particle accelerator operations, leading to reduced energy consumption and increased throughput.