Phase Shift

Phase shift, the adjustment of a signal's phase, is crucial in various fields, with current research focusing on optimizing its application to enhance signal processing and communication systems. Researchers are employing deep reinforcement learning (e.g., DDPG, TD3, MAQ), neural networks (including Vision Transformers), and other machine learning techniques (e.g., XGBoost, Federated Learning) to design optimal phase-shift strategies across diverse applications, from improving wireless communication and LiDAR depth resolution to enhancing power system protection and optical fiber communication. These advancements promise significant improvements in efficiency, accuracy, and robustness across numerous technological domains.