Energy Efficiency of Cooperative Spectrum Sensing Under Sensing Delay Constraint for CUAVNs

Zhang, Jia; Wu, Jun; Gan, Jipeng; Chen, Ze; He, Jiangtao; Chen, Zehao

doi:10.1109/vtc2022-spring54318.2022.9860525

Cited by 5 publications

(1 citation statement)

References 16 publications

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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.…”

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%

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

Yu,

Shen,

et al. 2024

Preprint

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Sequential detection with feedback information for two‐way cooperative spectrum sensing in cognitive internet of things

Wu,

Su,

Bao

et al. 2024

Trans Emerging Tel Tech

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With the rapid growth of internet of thing (IoT) devices, cooperative spectrum sensing (CSS) has emerged as a promising solution to leverage the spatial diversity of multiple secondary IoT sensing nodes (SNs) for spectrum availability. However, the cooperative paradigm also incurs increased cooperative costs between each SN and the fusion center (FC), leading to decreased cooperative efficiency and achievable throughput, especially in large‐scale cognitive IoT (CIoT). To address these challenges, we present a sequential detection with feedback information (SD‐FI) approach in this paper. To achieve this objective, we propose a two‐way CSS model that formulates an optimization problem of Bayes cost in a quickest detection framework with feedback. To solve this optimization problem, we derive the structure of the optimal local decision rule from the local decision function and determine the optimal detection threshold in conjunction with the cost function. Following the optimal threshold pair, we implement the optimal SD‐FI and theoretically demonstrate the uniqueness of the optimal threshold and optimal sensing time. Simulation results demonstrate superiority of SD‐FI in terms of cooperative performance (i.e., detection performance and Bayes cost) and sample size. Notably, even with limited sensing time, our proposed SD‐FI exhibits high throughput, highlighting its effectiveness in enhancing spectrum availability and utilization in CIoT.

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Deep learning-based selective spectrum sensing and allocation in cognitive vehicular radio networks

Paul

Choi

2023

Vehicular Communications

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Energy Efficiency of Cooperative Spectrum Sensing Under Sensing Delay Constraint for CUAVNs

Cited by 5 publications

References 16 publications

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

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

Sequential detection with feedback information for two‐way cooperative spectrum sensing in cognitive internet of things

Deep learning-based selective spectrum sensing and allocation in cognitive vehicular radio networks

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