  Unmanned aerial vehicles (UAVs) have brought a lot of flexibility in the
network deployment. However, UAVs suffer from the high mobility and
instability. To improve the capacity and reliability of the UAV networks,
millimeter-wave (mmWave) and reconfigurable intelligent surfaces (RISs) can be
used in the system. In this paper, we consider an RIS-assisted mmWave UAV
wireless cellular network, where UAVs serve several users with the help of
multiple RISs. We jointly optimize the deployment, user scheduling, beamforming
vector, and RIS phases to maximize the sum-rate, with the constraints of the
minimum rate, the UAV movement, the analog beamforming, and the RIS phases. To
solve this complex problem, we use an iterative method, in which when we
optimize one variable, we fix the other three variables. When optimizing the
deployment, we find the optimal position for the UAV by a sphere search. Then,
we formulate a mixed-integer non-linear problem (MINLP) to find the best
scheduling. A spatial branch-and-bound (sBnB) method is used to solve the
MINLP. When Optimizing the beamforming vector and the RIS phases, we propose an
iterative algorithm that relies on the equivalence between the maximization of
the sum-rate and the minimization of the summation of weighted mean-square
errors (sum-WMMSE). The majority-minimization method is used to deal with the
constant-modulus constraints for the analog beamforming and RIS phases. The
proposed joint optimization offers significant advantages over the system
without beamforming and RIS phase optimization and the system without
deployment optimization. In addition, the RIS can compensate for the loss of
throughput due to the blockage, especially in low flight altitudes.
