Analysis With Deep Learning of Robust UAV-Mounted Active IRS NOMA Networks With Imperfections

Abstract

This paper introduces a robust cooperative network where an active intelligent reflecting surface (A-IRS) mounted on an unmanned aerial vehicle (UAV) is employed in order to significantly enhance the air-to-ground communications. By utilizing advanced maneuver control and intelligent reflection, the network optimizes wireless channels, substantially improving spectrum efficiency through a non-orthogonal multiple access (NOMA) scheme. We consider non-ideal system imperfections, such as co-channel interference, hardware impairments, and imperfect successive interference cancellation. We derive the expressions for users’ outage probability (OP), ergodic capacity, and system throughput in both delay-limited and delay-tolerant modes under Nakagami fading channels, reflecting realistic channel variations. Additionally, we present an asymptotic OP analysis to gain useful insights into the high signal-to-noise ratio regime and diversity order, which are useful in optimizing network parameters for maximal reliability. Our study advances complex optimization problems for deep neural network (DNN) hyperparameters, power allocation, and UAV positioning, which are crucial for the dynamic aerial communication environment. We also introduce a new method to evaluate the robustness of our system, the analysis reveals that the system performs well with fewer IRS elements, optimizing the balance between energy efficiency and outage performance. Given the significant complexity of the proposed system model, directly deriving closed-form expressions for the OP and the ergodic sum capacity is a challenge. We develop a DNN framework that predicts OP and ergodic sum capacity in real-time scenarios to overcome this issue. Extensive simulations validate the derived expressions and demonstrate that a UAV-mounted A-IRS NOMA network outperforms both passive IRS NOMA setups and traditional relaying methods. These results affirm notable enhancements in reliability and performance, establishing the network’s superiority in modern wireless communication scenarios and underscoring its potential to enhance both service quality and economic viability in deploying advanced communication infrastructures.

Keywords

• Active intelligent reflecting surface
• co-channel interference
• deep neural network
• hardware impairments
• non-orthogonal multiple access
• unmanned aerial vehicle