Bayesian quickest detection-based spectrum sensing for cognitive UAV networks

With the gradual popularization of unmanned aerial vehicles (UAVs), spectrum resources are increasingly scarce. At this time, cognitive radio (CR) technology came into being, which can provide available spectrum resources for UAVs. However, different from the conventional CR networks, the UAV's location flexibility significantly reduces the spectrum sensing performance and increases the detection delay. Therefore, we propose a Bayesian quickest detection-based spectrum sensing for cognitive UAV networks in this paper. To this aim, we establish a multislot spectrum sensing model in a cognitive UAV network to realize cooperative spectrum sensing (CSS) within the single UAV and avoid disadvantages of cooperation among multiple UAVs. On the basis of this, we make use of Bayesian quickest detection to implement multislot spectrum sensing and evaluate its false alarm probability and detection delay. Further, the tradeoff problem between the false alarm and the detection delay is developed and converted into an optimization stopping time problem. Subsequently, the stopping time problem is theoretically proved and solved. Finally, compared to Bayesian detection, numerical simulation results show that the superiority of Bayesian quickest detection with respect to the false alarm probability and the detection delay is evident, which proves the correctness and effectiveness of the proposed tradeoff problem.

Automated summary

With the popularization of unmanned aerial vehicles (UAVs), the scarcity of spectrum resources has become an issue. To address this, a Bayesian quickest detection-based spectrum sensing method for cognitive UAV networks is proposed. By establishing a multislot spectrum sensing model, cooperative spectrum sensing (CSS) is achieved within a single UAV, avoiding the limitations of cooperation among multiple UAVs. Bayesian quickest detection is used to implement multislot spectrum sensing, and its false alarm probability and detection delay are evaluated. The tradeoff problem between false alarm and detection delay is formulated and solved.