  In this paper, an intelligent reflecting surface (IRS) is introduced to
assist an unmanned aerial vehicle (UAV) communication system based on
non-orthogonal multiple access (NOMA) for serving multiple ground users. We aim
to minimize the average total system energy consumption by jointly designing
the resource allocation strategy, the three dimensional (3D) trajectory of the
UAV, as well as the phase control at the IRS. The design is formulated as a
non-convex optimization problem taking into account the maximum tolerable
outage probability constraint and the individual minimum data rate requirement.
To circumvent the intractability of the design problem due to the
altitude-dependent Rician fading in UAV-to-user links, we adopt the deep neural
network (DNN) approach to accurately approximate the corresponding effective
channel gains, which facilitates the development of a low-complexity suboptimal
iterative algorithm via dividing the formulated problem into two subproblems
and address them alternatingly. Numerical results demonstrate that the proposed
algorithm can converge to an effective solution within a small number of
iterations and illustrate some interesting insights: (1) IRS enables a highly
flexible UAV's 3D trajectory design via recycling the dissipated radio signal
for improving the achievable system data rate and reducing the flight power
consumption of the UAV; (2) IRS provides a rich array gain through passive
beamforming in the reflection link, which can substantially reduce the required
communication power for guaranteeing the required quality-of-service (QoS); (3)
Optimizing the altitude of UAV's trajectory can effectively exploit the
outage-guaranteed effective channel gain to save the total required
communication power enabling power-efficient UAV communications.
