Abstract
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.
Highlights
A reconfigurable intelligent surface (RIS) is a promising technology to extend the coverage of the communication systems by means of passive antenna arrays [1]
An online single-player multiarmed bandit (MAB) game is constructed to address this problem efficiently. In this formulation, the base station (BS) is considered as the player of the bandit game; the available joint values of Φi and fj are the arms of the bandit; and the achievable spectral efficiency at the user equipment (UE), i.e., ψΦifj, is the reward
We have explored RIS-assisted Millimeter wave (mmWave) communications
Summary
A reconfigurable intelligent surface (RIS) is a promising technology to extend the coverage of the communication systems by means of passive antenna arrays [1]. To reduce the complexity of the arm optimization as we have two sets of arms, one belonging to the mmWave TX and the other to the RIS, a nested two-stage MAB game is proposed in this paper. In this approach, the PS vector of mmWave TX is adjusted in the first MAB stage, and based on it, the PS vector of the RIS is modified in the stage and vice versa over the time horizon.
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