2020/11/03 by Qianqian Zhang, Walid Saad, Zhang, Qianqian +3
Engineering · #Advanced Wireless Communication Technologies #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Information Theory (cs.IT) #Millimeter-Wave Propagation and Modeling #UAV Applications and Optimization
paper · pdf · doi:10.48550/arxiv.2011.01840
openalex publication_date 2020/11/03 · openalex created_date 2020/11/09 · openalex updated_date 2026/07/28
In this paper, a novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed to enhance multi-user downlink transmissions over millimeter wave (mmWave) frequencies. In order to maximize the downlink sum-rate, the optimal precoding matrix (at the base station) and reflection coefficient (at the IR) are jointly derived. Next, to address the uncertainty of mmWave channels and maintain line-of-sight links in a real-time manner, a distributional reinforcement learning approach, based on quantile regression optimization, is proposed to learn the propagation environment of mmWave communications, and, then, optimize the location of the UAV-IR so as to maximize the long-term downlink communication capacity. Simulation results show that the proposed learning-based deployment of the UAV-IR yields a significant advantage, compared to a non-learning UAV-IR, a static IR, and a direct transmission schemes, in terms of the average data rate and the achievable line-of-sight probability of downlink mmWave communications.