An optimization based approach to enhance the throughput and energy efficiency for cognitive unmanned aerial vehicle networks

Unmanned aerial vehicle (UAV) is a promising technology to serve as a mission-critical communication, where human operation or intervention is risky, dangerous, impossible or critical in terms of time and cost. The performance of the UAV depends highly on the operating radio environment e.g. multipath shadowing and fading, and spectrum availability. Therefore, a cognitive radio-based UAV is considered to utilize the radio spectrum opportunistically, and minimize the network congestion during a disaster on preallocated channel or degradation of the quality of service. In this paper, we have presented an analytical model for cognitive UAV downlink communication between the UAV and the ground nodes. This model aims to maximize the throughput and improve the energy efficiency by minimizing the power consumption for the UAVs. Specifically, we developed two optimization approaches to address the energy efficiency with and without the throughput of the user as constraints. Simulation results for the energy efficiency without constraints show that $$11.16\%$$ and $$6.43\%$$ performance improvement can be achieved for throughput, and energy efficiency respectively in comparison to unoptimized one. Similar behavior has observed with the energy efficiency with constraints and a significant improvement has achieved in throughput performance e.g. $$64.02\%$$ with the cost of energy.