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A dynamic Q-learning beamforming method for inter-cell interference mitigation in 5G massive MIMO networks
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Authors: Aidong Yang, Xinlang Yue, Mohan Wu, Ye Ouyang
Status: Final
Date of publication: 5 August 2021
Published in: ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4 - AI and machine learning solutions in 5G and future networks, Pages 47-55
Article DOI : https://doi.org/10.52953/KXFT3962
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 | Abstract:
Beamforming is an essential technology in 5G Massive Multiple-Input Multiple-Output (MMIMO) communications, which are subject to many impairments due to the nature of wireless transmission channel. The Inter-Cell Interference (ICI) is one of the main obstacles faced by 5G communications due to frequency-reuse technologies. However, finding the optimal beamforming parameter to minimize the ICI requires infeasible prior network or channel information. In this paper, we propose a dynamic Q-learning beamforming method for ICI mitigation in the 5G downlink that does not require prior network or channel knowledge. Compared with a traditional beamforming method and other industrial Reinforcement Learning (RL) methods, the proposed method has lower computational complexity and better convergence efficiency. Performance analysis shows the quality of service improvement in terms of Signal-to-Interference-plus-Noise-Ratio (SINR) and the robustness towards different environments. |
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Keywords: 5G beamforming, inter-cell interference, massive MIMO, reinforcement learning
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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