AI-Based Radio Resource Management and Trajectory Design for IRS-UAV-Assisted PD-NOMA Communication

This paper proposes the use of unmanned aerial vehicles (UAVs) with intelligent reflecting surfaces (IRS) to reflect signals from the industrial internet of things (IIoT) to the destination, where power-domain non-orthogonal multiple access (PD-NOMA) is used in the uplink. The objective of our paper is to minimize the average age of information (AAoI) of users affected by transmit power constraint, and UAV movement restrictions. By optimizing transmit power, sub-carriers, trajectory, and phase shift matrix elements, UAV-IRS on IIoT networks can improve the freshness of the data collected from IIoT devices. The nonlinear integer optimization problem leads to an NP-hard problem, which is practically difficult to solve. We exploit the powerful reinforcement learning algorithm, i.e., the proximal policy optimization (PPO). The numerical results illustrate the benefits of IRS-enabled UAV communication systems. By using IRSs and the PPO algorithm, UAVs can achieve better performance than other methods that consider a fixed IRS, random deployment, other RL methods(A2C), and the impact of UAV jitter.