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

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 realtime 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.

Authors:
Zhang Qianqian, Saad Walid, Bennis Mehdi

Publication type:
A4 Article in conference proceedings

Place of publication:
GLOBECOM 2020 – 2020 IEEE Global Communications Conference

Keywords:
6G Publication

Published:

Full citation:
Q. Zhang, W. Saad and M. Bennis, “Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV,” GLOBECOM 2020 – 2020 IEEE Global Communications Conference, Taipei, Taiwan, 2020, pp. 1-6, doi: 10.1109/GLOBECOM42002.2020.9348040

DOI:
https://doi.org/10.1109/GLOBECOM42002.2020.9348040

Read the publication here:
http://urn.fi/urn:nbn:fi-fe202102255937