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

5G 和 6G 之外系统中毫米波通信的自适应机器学习算法

• 批准号: 21K14162
• 负责人: Hashima Sherief
• 金额: $ 2.91万
• 依托单位: Institute of Physical and Chemical Research
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2021
• 资助国家: 日本
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K14162/
• 关键词: RIS; UAV-mounted RIS; RIS relaying; Hybrid band RF/VLC; Load Balancing; MAB; PHE; RUCB, ETC; D2D Communications; millimeter wave (mmWave); radio frequency (RF); multi-armed bandit (MAB); UCB; Thompson sampling (TS); MOSS; WSNs; B5G; 6G

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.

混合带RF/VLC优化问题是使用预算约束的mAB提出的，并进行了不同的mAb溶液之间的比较。此外，我们提出了用于无人机固定的RIS轨迹计划的算法，以最大化数据速率并最大程度地减少无人机的能源消耗。此外，我们将复杂的MAB技术（PHE和MOT）应用于相同的问题，并具有出色的性能结果。实现了双向客观匪徒，以最大化BS-user NLOS链接数据速率，以最大程度地提高BEAFForm训练时间，并最大程度地减少MMWave RIS ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris ris sceam的问题。部署的RIS委员会。三个集中的MP-MAB算法，具有武器的负载对齐，来自上限型（UCB）家族（UCB），即UCB1-LB，Kullback-Leibler UCB-LB（KLUCB-LB）（KLUCB-LB），以及最小值的最佳随机策略策略LB（Moss-LB（Moss-lb），效果均可与形式相吻合。

项目成果

Cost-Effective MAB Approaches for Reconfigurable Intelligent Surface Aided Millimeter Wave Relaying

• DOI: 10.1109/access.2022.3195303
• 发表时间: 2022-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Mohamed, Ehab Mahmoud;Hashima, Sherief;Fouda, Mostafa M.
• 通讯作者: Fouda, Mostafa M.

Optimal Channel Selection in Hybrid RF/VLC Networks: A Multi-Armed Bandit Approach

• DOI: 10.1109/tvt.2022.3163078
• 发表时间: 2022-06
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: M. Fouda;S. Hashima;S. Sakib;Z. Fadlullah;Kohei Hatano;X. Shen

Idaho State University(米国)

爱达荷州立大学（美国）

Sleeping Contextual/Non-Contextual Thompson Sampling MAB for mmWave D2D Two-Hop Relay Probing

• DOI: 10.1109/tvt.2021.3116223
• 发表时间: 2021-11
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: E. M. Mohamed;S. Hashima;Kohei Hatano;M. Fouda;Z. Fadlullah

ORCID webpage

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