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

利用预算约束MAB对混合频段RF/VLC优化问题进行了建模，并对不同MAB方案进行了比较。此外，我们提出了算法的无人机安装RIS轨迹规划，最大限度地提高数据速率，最大限度地减少无人机的能源消耗。此外，我们认为，我们应用了复杂的MAB技术针对多RIS系统中的毫米波RIS-用户关联问题，提出了一种基于双目标的RIS中继探测算法，以最大化基站-用户NLOS链路数据速率和最小化波束形成训练时间，并给出了算法的仿真结果。用户场景被认为是最大化用户可达到的数据速率，同时保持部署的RIS板之间的负载平衡。三个集中式MP-MAB算法与手臂的负载平衡，分别从置信上界（UCB）家族中提出了三种策略，即UCB 1-LB、Kullback-Leibler UCB-LB（KLUCB-LB）和Minimax最优随机策略-LB（MOSS-LB），并对它们的性能进行了比较。

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.