Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks

The latter mainly includes first Monte Carlo (MC) (MMIMO) communications, which are subject method, which searches the optimal beamforming parameters but to many impairments due to the nature of wireless transmission suffers from increasing computational complexity, and second deep channel, i.e. the air. The inter-cell interference (ICI) is one of the learning (DL) methods. One of them is reported in [8] to research main impairments faced by 5G communications due to frequencyreuse the characters of wireless spatial channels and explore preferable technologies. In this paper, we propose a reinforcement learning pilot assignments for better channel estimation and beamforming, (RL) assisted full dynamic beamforming for ICI mitigation in 5G but DL methods require training algorithmic model beforehand and downlink. The proposed algorithm is a joint of beamforming and time-consuming sample data collection.