Joint Resource, Trajectory, and Artificial Noise Optimization in Secure Driven 3-D UAVs With NOMA and Imperfect CSI

Driven by the practicality of unmanned aerial vehicle (UAV), we consider a dual-UAV based non-orthogonal multiple access (NOMA) scenario, which consists of one communication UAV for services and one jamming UAV against eavesdropping. The goal is to maximize the secrecy energy efficiency through the successive convex approximation based communication resource, UAV trajectory, and artificial noise optimization. Considering the probabilistic constraint of outage probability from imperfect channel state information, we transform it to a non-probabilistic problem by Markov inequality and Marcum $Q$ -function, then the problem is decomposed into three subproblems. We apply matching-swapping method to assign subchannel in non-orthogonal multiple access (NOMA) UAV networks before the joint process, then convert the power optimization problem to a standard convex optimization form by upper bound of the concave function. The communication UAV trajectory is studied under the constraints of flying energy consumption, maximum speed, and flying altitude. To track this NP-hard problem, Taylor expansion and various slack variables sets are introduced to transform the non-convex problem to convex one. For the artificial noise optimization problem, we use the lower bound to replace the convex term turning it into an easy-to-solve convex optimization problem. In the end, simulations results reveal that: 1) The reasonable jamming scheme can improve the secrecy energy efficiency of the NOMA UAV networks, even if it can cause interference for legitimate users; 2) UAV will fly to a place where the performance gain from users is high when flying energy consumption permits.

Automated summary

This study proposes a NOMA scenario based on dual UAVs to maximize secrecy energy efficiency through optimizing communication resources, UAV trajectories, and artificial noise. By applying matching-swapping method and upper/lower bound transformation, the joint optimization problem is effectively solved.