Throughput Maximization of a UAV Assisted CR Network With NOMA-Based Communication and Energy-Harvesting

Abstract

The performance of an unmanned aerial vehicle (UAV) assisted cooperative cognitive radio (CR) network is studied in this paper. The study is carried out for line-of-sight (LOS) links between UAV and a ground user and UAV aerodynamic power consumption of UAV. The UAV consists of an RF energy harvester and it is also a CR enabled device. It harvests energy from RF signal of source nodes. It rotates periodically over a circular region of interest (CROI) for finding the vacant primary user (PU) band. It uses a PU band to set a link between ground nodes, while the direct links between them suffer from severe fading. The decision about the status of a PU band is made cooperatively. In cooperative sensing, all the sources along with the UAV take sensing decisions and send them to fusion centre (FC). A two stage fusion scheme is proposed to take the overall decision. The considered network model uses non-orthogonal multiple access (NOMA) scheme to forward the information of a source to its corresponding destination. Analytical expressions of harvested energy, overall sensing decision and throughput are developed. The analytical expressions capture the effect of fading as well as change in distances (source-UAV and UAV-destination) due to the circular flight of the UAV. Impact of imperfect successive interference cancellation (SIC) on the system performance is also studied. Power allocation in the second hop link is optimized to maximize the network throughput. Energy-efficiency of the considered network is evaluated. For a given UAV flight period, radius of the circular trajectory, and the velocity of the UAV is optimized to maximize the energy-efficiency. Sensing performance of the considered system model is studied in terms of false alarm. Network throughput is investigated for several network parameters such as fading parameter, the number of sources, the number of UAV's sensing slots, the velocity of UAV, the radius of the UAV trajectory etc. Sensing angle and harvesting angle are optimized to maximize the throughput. Results show that the value of fading parameter, radius of UAV trajectory, and UAV velocity has significant impact on throughput. The performance of NOMA is compared with orthogonal multiple access (OMA).