JOINT DESIGN OF USER ASSOCIATION AND HYBRID BEAMFORMING METHOD AND SYSTEM FOR SUB-THz UDN USING MULTI-AGENT DEEP REINFORCEMENT LEARNING

A non-transitory computer-readable recording medium storing a computer program to implement an operation method of an interference control and hybrid beamforming system using multi-agent deep reinforcement learning for multiple users, comprising:

• performing multi-agent reinforcement learning using interference and antenna gain information that is expected based on a gain table designed using channel state information (CSI) of all user equipments;
• searching for an analog beamforming matrix pair corresponding to a link that maximizes antenna gain and minimizes interference between user equipments through multi-agent reinforcement learning;
• applying a signal-to-leakage plus noise ratio (SLNR) maximization technique that minimizes the interference between the user equipments based on the link;
• and
• optimizing transmission (Tx) power for each link based on iterative water-filling.