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Millimeter-Wave Concurrent Beamforming:A Multi-Player Multi-Armed Bandit Approach

Computers, Materials & Continua 2020年第12期65卷 1987-2007页

作者： Ehab Mahmoud Mohamed Sherief Hashima Kohei Hatano hani kasban Mohamed Rihan Electrical Engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia Electrical Engineering Department Faculty of EngineeringAswan UniversityAswan81542Egypt Computational Learning Theory Team RIKEN-Advanced Intelligent ProjectFukuoka819-0395Japan Engineering Department Nuclear Research CenterEgyptian Atomic Energy AuthorityCairo13759Egypt Faculty of Arts and Science Kyushu UniversityFukuok819-0395Japan Electronics and Electrical Communication Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The communication in the Millimeter-wave(mmWave)band,i.e.,30~300 GHz,is characterized by short-range transmissions and the use of antenna beamforming(BF).Thus,multiple mmWave access points(APs)should be installed to fully cover a target environment with gigabits per second(Gbps)connectivity.However,inter-beam interference prevents maximizing the sum rates of the established concurrent links.In this paper,a reinforcement learning(RL)approach is proposed for enabling mmWave concurrent transmissions by finding out beam directions that maximize the long-term average sum rates of the concurrent links.Specifically,the problem is formulated as a multiplayer multiarmed bandit(MAB),where mmWave APs act as the players aiming to maximize their achievable rewards,i.e.,data rates,and the arms to play are the available beam directions.In this setup,a selfish concurrent multiplayer MAB strategy is advocated.Four different MAB algorithms,namely,ϵ-greedy,upper confidence bound(UCB),Thompson sampling(TS),and exponential weight algorithm for exploration and exploitation(EXP3)are examined by employing them in each AP to selfishly enhance its beam selection based only on its previous observations.After a few rounds of interactions,mmWave APs learn how to select concurrent beams that enhance the overall system performance.The proposed MAB based mmWave concurrent BF shows comparable performance to the optimal solution.

关键词： Millimeter wave(mmWave) concurrent transmissions reinforcement learning multiarmed bandit(MAB)

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