Optimal Utility of Cooperative Spectrum Sensing for CUAVNs

Wu, Jun; Zhang, Jia; Chen, Zehao

doi:10.1109/vtc2021-spring51267.2021.9448941

Cited by 3 publications

(1 citation statement)

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“…In [9], J. Zhang et al developed a CSS framework among mini-sensing slots for CUAVNs under flexible sensing delay constraints. In [10], J. Wu et al proposed a multi-slot CSS among multiple minislots of a UAV sensing node and analyzed its performance and cost/benefit. A multi-UAV spectrum sensing algorithm based on group cooperation was proposed by Z. Liu in [11], which improves the detection probability based on the combination of feature extraction and clustering algorithm.…”

Section: Related Work and Motivationmentioning

confidence: 99%

SVM Dynamic Selection of Voting Rule for Cooperative Spectrum Sensing in CUAVNs

Yu,

Shen,

et al. 2024

Preprint

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Due to the rapid development of unmanned aerial vehicles (UAVs) communication technology, UAVs are gradually competing with primary users (PUs) for spectrum resources. Cognitive radio (CR) is a promising solution to meet the needs. Cooperative spectrum sensing (CSS) is considered as an effective method to detect the PU signal and identify available spectrum resources for UAVs in a cognitive UAV network (CUAVN). However, the cooperative mode among multiple UAVs may incur a high overhead, resulting in performance degradation. Therefore, we introduce a differential sequential 1 (DS1), which incorporates a differential mechanism and leverages the sequential idea based on the classical voting rule to enhance cooperative performance and efficiency. In view of this, we formulate three scenarios to characterize the PU activity and introduce a multi-slot cooperative mode within a single UAV to realize cooperative gain. Further, only the information change about the PU status is sequentially calculated in DS1, and combined with a sequential idea, the efficiency of the voting rule is greatly improved. Moreover, the application of support vector machines (SVM) in dynamic selection enables the selection of the most suitable voting rule based on diverse sensing parameters. This dynamic selection process ensures optimal performance and efficiency by adapting the voting rule to the specific characteristics of the given scenario. Finally, simulation results demonstrate that the superiority of our proposal with respect to the detection performance, sample size and the energy efficiency is evident, which proves the high performance of the proposed policy.

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Section: Related Work and Motivationmentioning

confidence: 99%