UAV Based Optimized Virtual Cooperative Sensing Using Particle Swarm Optimization

Spectrum sensing utilizing unmanned aerial vehicles (UAVs) has become increasingly popular due to their advantageous line of sight (LoS) communication links. In traditional cognitive radio networks (CRNs), secondary users (SUs) opportunistically access the primary user (PU) channel through cooperative spectrum sensing, aiming to ensure reliable sensing while minimizing disturbances for licensed users. In this study, we evaluate an overlay mode of the CRN where a UAV acts as the SU. Instead of employing multiple SUs as in a terrestrial cooperative spectrum sensing setup with a fusion center (FC), our approach involves a single UAV performing virtual cooperative sensing by following a circular flight trajectory. During the UAV’s sensing period, it consists of virtual mini-sensing slots akin to a group of SUs. The UAV enhances sensing reliability by performing local spectrum sensing within each mini-slot and combines the collected data using the voting scheme to make collective decisions. Moreover, the mini-slot sensing radian is optimized using particle swarm optimization (PSO) to readjust the sensing time. The optimized sensing time has resulted in increased throughput and reduce sensing time for the virtual cooperative sensing environments.