  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.
