Combined UAV positioning with robust beamforming and IRS-enhanced NOMA transmission in cognitive UAV networks

Abstract

Cognitive unmanned aerial vehicle networks (CUAVNs) are considered as a promising technology to provide ubiquitous connectivity for various users. This paper proposes a robust downlink transmission scheme for intelligent reflecting surface (IRS) assisted CUAVN. Here, the UAV in aerial network exploits space division multiple access and IRS-enhanced non-orthogonal multiple access technology to offer ubiquitous connectivity for UAV users. By assuming that the UAV network shares the same frequency band with primary users (PUs), and only the angular information based imperfect channel state information of each user is available, we first formulate an optimization problem aiming at maximizing the sum achievable rate of all UAV users subject to the constraint of quality-of-service requirement for each user, co-channel interference constraints at PUs, per-antenna transmit power budgets of UAV, and unit-modulus requirement for each reflecting element. To tackle this mathematically intractable problem, we first propose an UAV positioning algorithm based on the ideal beampatterns to independently obtain the optimal UAV position. And then, we employ angular discretization joint with the trace-conversion-based successive convex approximation method and semi-definite relaxation approach to obtain the active beamforming vectors of UAV, the passive beamforming vector of IRS, and the power allocation coefficients. Finally, simulation results are given to demonstrate the effectiveness and superiority of the proposed robust scheme compared with benchmarks.