Outage Analysis of UAV-Assisted Cooperative Cognitive NOMA in IoT-Enabled Air–Ground Networks With Imperfect SIC and CSI

This paper addresses the challenges faced by air-ground vehicle networks (AGVN), with a focus on the rapid mobility of both unmanned ground vehicle (UGV) and unmanned aerial vehicles (UAVs) clusters. UAVs employ the non-orthogonal multiple access (NOMA) transmission technique to efficiently transfer data to UGV, aiming to enhance network performance by increasing spectral efficiency and supporting simultaneous transmissions. The system integrates UAV into the network perception environment, enabling cognitive activities and convenient transmission for UGV beyond base station coverage. We provide closed-form outage probability (OP) expressions for UAVs and UGV in two assist modes, i.e., amplify-and-forward UAV (A-UAV) and decode-and-forward UAV (D-UAV), using real-world situations with double Rayleigh fading (DRF) and outdated and imperfect channel state information (ipCSI). Additionally, in both relaying modes, the asymptotic expression for the OP is provided in the high SNR regime for UAV and UGV links. In addition, the system’s diversity order is derived, and the asymptotic analysis shows an intricate interplay of relative speed, channel outdatedness, and OP. Our research results show that when the relative speed decreases or the power increases, the OP decreases. Under the same conditions, the performance of A-UAV is more stable than that of D-UAV. Additionally, the study underscores the importance of assist mode and the number of UAVs in achieving optimal outage performance. Simulation results confirm our analytical results, emphasizing the trade-offs and performance indicators for AGVN system design. The study demonstrates potential avenues for maximizing system performance even within stringent constraints through strategic parameter adjustments.