SPRT-based cooperative spectrum sensing with performance requirements in cognitive unmanned aerial vehicle networks (CUAVNs)

Abstract

In view of the spectrum scarcity of unmanned aerial vehicles’ (UAVs) communication systems, cooperative spectrum sensing (CSS) is used to identify the available spectrum for cognitive UAV networks (CUAVNs). Due to the flexible locations of flying UAVs, we first design an intraframe cooperation way to replace the traditional interframe cooperation to achieve CSS in this article. Considering certain performance requirements in CUAVNs, we perform an in-depth analysis of the relationship among performance indices of sequential probability ratio test (SPRT)-based CSS and approximate the actual detection performance and the stopping time by ignoring the excess over the boundaries and the random walk. Finally, numerical results corroborate the effectiveness and correctness of our theoretical analysis, and comparison results represent the superiority of our proposal.

Keywords:

• Cooperative spectrum sensing
• cognitive unmanned aerial vehicular network
• sequential probability ratio test
• stopping time

SUBJECT CLASSIFICATIONS:

• 62L15
• 60G40
• 62F12
• 62F15

ACKNOWLEDGMENTS

The authors thank the anonymous referees and the Associate Editor for valuable suggestions on improving an early version of this article.

DISCLOSURE

The authors have no conflicts of interest to report.

Additional information

Funding

This work is supported by the National Natural Science Foundation of Zhejiang Province (No. LQ22F010013), Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (No. 2022D16), Open Fund Project of Sichuan Provincial Key Laboratory of Artificial Intelligence (No. 2021RYJ07), Fundamental Research Funds for the Provincial Universities of Zhejiang (Nos. GK209907299001-023 and GK219909299001-027), National Natural Science Foundation of China (No. U1809201), and 2020 Annual Teachers’ Professional Development Project of Domestical Visiting Scholars in Institutions of Higher Education (No. FX2020009).