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

作     者：Ehab Mahmoud Mohamed Sherief Hashima Kohei Hatano Hani Kasban Mohamed Rihan 

作者机构：Electrical Engineering DepartmentCollege of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia Electrical Engineering DepartmentFaculty of EngineeringAswan UniversityAswan81542Egypt Computational Learning Theory TeamRIKEN-Advanced Intelligent ProjectFukuoka819-0395Japan Engineering DepartmentNuclear Research CenterEgyptian Atomic Energy AuthorityCairo13759Egypt Faculty of Arts and ScienceKyushu UniversityFukuok819-0395Japan Electronics and Electrical Communication EngineeringFaculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2020年第65卷第12期

页      面：1987-2007页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

基　　金：The paper is fully funded by RIKEN-AIP. 

主　　题：Millimeter wave(mmWave) concurrent transmissions reinforcement learning multiarmed bandit(MAB) 

摘      要：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.