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.
@article{arxiv.2103.11782,
title = {Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks},
author = {Aidong Yang and Xinlang Yue and Ye Ouyang},
journal= {arXiv preprint arXiv:2103.11782},
year = {2021}
}
Comments
There is an error in section 4 about what are the definitions of states and actions, which will affect the performance of the algorithm