  Beamforming is an essential technology in the 5G massive
multiple-input-multiple-output (MMIMO) communications, which are subject to
many impairments due to the nature of wireless transmission channel, i.e. the
air. The inter-cell interference (ICI) is one of the main impairments faced by
5G communications due to frequency-reuse technologies. In this paper, we
propose a reinforcement learning (RL) assisted full dynamic beamforming for ICI
mitigation in 5G downlink. The proposed algorithm is a joint of beamforming and
full dynamic Q-learning technology to minimize the ICI, and results in a
low-complexity method without channel estimation. Performance analysis shows
the quality of service improvement in terms of
signal-to-interference-plus-noise-ratio (SINR) and computational complexity
compared to other algorithms.
