Mohamed Ehab Mahmoud, Hashima Sherief, Hatano Kohei, Aldossari Saud Alhajaj
Electrical Engineering Department, College of Engineering at Wadi Addwasir, Prince Sattam Bin Abdulaziz University, Wadi Addwasir 11991, Saudi Arabia.
Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt.
Sensors (Basel). 2022 Mar 10;22(6):2179. doi: 10.3390/s22062179.
A reconfigurable intelligent surface (RIS) is a promising technology that can extend short-range millimeter wave (mmWave) communications coverage. However, phase shifts (PSs) of both mmWave transmitter (TX) and RIS antenna elements need to be optimally adjusted to effectively cover a mmWave user. This paper proposes codebook-based phase shifters for mmWave TX and RIS to overcome the difficulty of estimating their mmWave channel state information (CSI). Moreover, to adjust the PSs of both, an online learning approach in the form of a multiarmed bandit (MAB) game is suggested, where a nested two-stage stochastic MAB strategy is proposed. In the proposed strategy, the PS vector of the mmWave TX is adjusted in the first MAB stage. Based on it, the PS vector of the RIS is calibrated in the second stage and vice versa over the time horizon. Hence, we leverage and implement two standard MAB algorithms, namely Thompson sampling (TS) and upper confidence bound (UCB). Simulation results confirm the superior performance of the proposed nested two-stage MAB strategy; in particular, the nested two-stage TS nearly matches the optimal performance.
可重构智能表面（RIS）是一种很有前景的技术，它可以扩展短程毫米波（mmWave）通信覆盖范围。然而，毫米波发射机（TX）和RIS天线元件的相移（PS）都需要进行优化调整，以有效地覆盖毫米波用户。本文提出了用于毫米波TX和RIS的基于码本的移相器，以克服估计其毫米波信道状态信息（CSI）的困难。此外，为了调整两者的PS，建议采用多臂赌博机（MAB）游戏形式的在线学习方法，其中提出了一种嵌套的两阶段随机MAB策略。在所提出的策略中，毫米波TX的PS向量在第一个MAB阶段进行调整。基于此，RIS的PS向量在第二阶段进行校准，反之亦然，在整个时间范围内交替进行。因此，我们利用并实现了两种标准的MAB算法，即汤普森采样（TS）和上置信界（UCB）。仿真结果证实了所提出的嵌套两阶段MAB策略的优越性能；特别是，嵌套两阶段TS几乎与最优性能相匹配。
