Joint location and transmit power optimization for NOMA-UAV networks via updating decoding order

Unmanned aerial vehicle (UAV) can be combined with non-orthogonal multiple access (NOMA) to achieve better performance. However, jointly optimizing the location, transmit power and decoding order for NOMA-UAV networks remains difficult, due to the change of decoding order as a result of UAV mobility. In this letter, a low-complexity scheme is proposed to maximize the sum rate of NOMA-UAV networks via updating decoding order, which can be decomposed into two steps. First, the joint location and power optimization can be divided into two non-convex sub-problems, which are further approximated via successive convex optimization. Then, the decoding order is updated according to the optimized UAV location. An iterative algorithm is proposed to execute the two steps alternately. In addition, the asymptotic performance is analyzed. Simulation results demonstrate the effectiveness of the proposed scheme

Security enhancement for NOMA-UAV networks

Owing to its distinctive merits, non-orthogonal multiple access (NOMA) techniques have been utilized in unmanned aerial vehicle (UAV) enabled wireless base stations to provide effective coverage for terrestrial users. However, the security of NOMA-UAV systems remains a challenge due to the line-of-sight air-to-ground channels and higher transmission power of weaker users in NOMA. In this paper, we propose two schemes to guarantee the secure transmission in UAV-NOMA networks. When only one user requires secure transmission, we derive the hovering position for the UAV and the power allocation to meet rate threshold of the secure user while maximizing the sum rate of remaining users. This disrupts the eavesdropping towards the secure user effectively. When multiple users require secure transmission, we further take the advantage of beamforming via multiple antennas at the UAV to guarantee their secure transmission. Due to the non-convexity of this problem, we convert it into a convex one for an iterative solution by using the second order cone programming. Finally, simulation results are provided to show the effectiveness of the proposed scheme