Spatiotemporal Cooperative Spectrum Sensing of Random Arrival of Primary Signals for Cognitive UAV Communications
Yuhan Liu, Ming Jin, Qinghua Guo · IEEE Sensors Journal · 2024
Recently, cognitive unmanned aerial vehicle (UAV) communications, which exploit unlicensed spectrum opportunistically, have garnered significant attention. Spectrum sensing serves as a critical function in cognitive UAV communications, aimed at averting unacceptable interference to primary users (PUs). Due to their widespread deployment across a wide area, the arrival time instants of primary signals at UAVs tend to be inconsistent in UAV spectrum sensing, which degrades the performance of conventional cooperative spectrum sensing (CSS) detectors. To address this issue, we propose a spatiotemporal weighted CSS detector in this work. When determining temporal weights, we take into account the distance disparities between UAVs and the PU. When determining spatial weights, we take into account both the elevation angles and distances of UAVs relative to the primary user (PU), which are statistically linked to signal-to-noise ratios (SNRs). By combining temporal and spatial weights, we achieve our spatiotemporal weights. Unlike conventional CSS detectors, the proposed detector mandates each UAV to transmit only one value to the fusion center, thereby significantly reducing transmission overhead. Moreover, we assess the theoretical performance of the proposed detector in terms of false alarm and detection probabilities. Numerical results validate our theoretical analyses and illustrate the superior performance of the proposed detector compared to contemporary detectors.