Joint Beamforming
Joint beamforming optimizes the transmission and reception of signals across multiple antennas to enhance communication efficiency and reliability. Current research heavily focuses on developing efficient algorithms, often leveraging deep reinforcement learning and graph neural networks, to solve the complex optimization problems inherent in coordinating multiple antennas, particularly in challenging environments like those involving unmanned aerial vehicles or satellite communication. These advancements are significant because they improve data rates, reduce energy consumption, and enable new applications in areas such as federated learning and ground-to-space communication, where efficient and robust signal processing is crucial.
Papers
UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning
Saichao Liu, Geng Sun, Jiahui Li, Shuang Liang, Qingqing Wu, Pengfei Wang, Dusit Niyato
Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning
Jiahui Li, Geng Sun, Qingqing Wu, Dusit Niyato, Jiawen Kang, Abbas Jamalipour, Victor C. M. Leung