Blockage Prediction

Blockage prediction focuses on anticipating disruptions in communication signals or surgical workflows, aiming to improve reliability and efficiency. Current research heavily utilizes deep learning, employing architectures like Vision Transformers, Convolutional Neural Networks, and Recurrent Neural Networks, often combined with multimodal data sources such as radar, LiDAR, and cameras, to achieve high-accuracy predictions. These advancements have significant implications for various fields, including improving the safety and effectiveness of laparoscopic surgery and enhancing the reliability of 5G/6G vehicular and mmWave communication networks by enabling proactive handover and beam management.