Adaptive Machine Learning Algorithms for mmWave Communications in Beyond 5G and 6G Systems

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Hybrid Band RF/VLC optimization problem was formulated using budget constrained MABs and comparsion between different MAB solutions were conducted. Also, we proposed algorithm for UAV mounted RIS trajectory planning that maximizes the data rate and minimizes UAV energy consumption. Besides, we applied sophisticated MAB techniques (PHE and MOTS) to the same problem with superior performance outcome.Dual objective bandits were implemented to RIS relay probing to maximize the BS-user NLOS linkage data rate and minimize the beaform training time.The problem of mmWave RIS-user association in muliple RIS multi-user scenarios is considered to maximize users’ achievable data rates while maintaining load balance among the deployed RIS boards.Three centralized MP-MAB algorithms with arms’ load alancing, coming from the family of upper confidence bound (UCB), namely UCB1-LB, Kullback-Leibler UCB-LB (KLUCB-LB), and minimax optimal stochastic strategy-LB (MOSS-LB), are proposed to address the formulated bandit game and to compare their performance.