Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV

• URL: http://arxiv.org/abs/2011.01840v1
• Date: Tue, 3 Nov 2020 16:50:37 GMT
• Title: Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV
• Authors: Qianqian Zhang, Walid Saad, Mehdi Bennis
• Abstract summary: A novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed. 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.
• Score: 119.97450366894718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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