  In this paper, we study the problem of joint active and passive beamforming
for reconfigurable intelligent surface (RIS)-assisted massive multiple-input
multiple-output systems towards the extension of the wireless cellular coverage
in 3D, where multiple RISs, each equipped with an array of passive elements,
are deployed to assist a base station (BS) to simultaneously serve multiple
unmanned aerial vehicles (UAVs) in the same time-frequency resource of 5G
wireless communications. With a focus on ensuring fairness among UAVs, our
objective is to maximize the minimum signal-to-interference-plus-noise ratio
(SINR) at UAVs by jointly optimizing the transmit beamforming parameters at the
BS and phase shift parameters at RISs. We propose two novel algorithms to
address this problem. The first algorithm aims to mitigate interference by
calculating the BS beamforming matrix through matrix inverse operations once
the phase shift parameters are determined. The second one is based on the
principle that one RIS element only serves one UAV and the phase shift
parameter of this RIS element is optimally designed to compensate the phase
offset caused by the propagation and fading. To obtain the optimal parameters,
we utilize one state-of-the-art reinforcement learning algorithm, deep
deterministic policy gradient, to solve these two optimization problems.
Simulation results are provided to illustrate the effectiveness of our proposed
solution and some insightful remarks are observed.
