Beamforming for maximal coverage in mmWave drones: a reinforcement learning approach

Vaezy, Hossein and Salehi Heydar Abad, Mehdi and Erçetin, Özgür and Yanikomeroglu, Halim and Omidi, Mohammad Javad and Naghsh, Mohammad Mahdi (2020) Beamforming for maximal coverage in mmWave drones: a reinforcement learning approach. IEEE Communications Letters, 24 (5). pp. 1033-1037. ISSN 1089-7798 (Print) 1558-2558 (Online)

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Drone as a base station can provide wireless services in a variety of situations. In this letter, we employ a uniform linear array (ULA) to produce a directional beam to increase the quality of service (QoS) of users in the downlink of cellular networks. Due to the strict power limitations of a drone base station (DBS), we envision a single radio frequency (RF) chain architecture. A beamforming design methodology in an unknown environment is presented over a mmWave channel with the aim of maximizing the number of covered users while taking into account the human body blockage effects. Regarding the ambiguity of the environment, we model the problem of finding the optimal beam direction as a multi-Armed bandit (MAB). Due to its fast convergence property, Thompson sampling (TS) is used for solving the MAB problem. Simulation results show that the DBS is able to find the optimal beam angle in only tens of iterations.
Item Type: Article
Uncontrolled Keywords: Beamforming; mmWave; multi-Armed bandit
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Özgür Erçetin
Date Deposited: 30 Jul 2023 16:51
Last Modified: 30 Jul 2023 16:51

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