Multi-User Beamforming with Deep Reinforcement Learning in Sensing-Aided Communication
2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)(2025)
关键词
Beamforming,Deep Reinforcement Learning,Precise Estimates,Prior Information,Heuristic Method,State Evolution,User Feedback,Cramer-Rao Lower Bound,Multiple Beams,Angle Of Departure,Objective Function,Dynamic Environment,Types Of Users,Antenna Array,Reward Function,Codeword,User Equipment,Discrete Action,Radar Cross Section,Beam Center,Transmission Time Interval,Proximal Policy Optimization,Dynamic Management,Deep Reinforcement Learning Model,Cluttered Environments,End Of Frame,Medium Access Control,Deep Reinforcement Learning Algorithm,User Power,Signal-to-interference-plus-noise Ratio
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