Cost-benefit Analysis of Cooperative Spectrum Sensing Under Detection Delay Constraint for CUAVNs

Wu, Jun; Li, Pei; Bao, Jianrong; Song, Tiecheng

doi:10.1109/wcncw49093.2021.9419989

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Intra-frame structure-based cognitive UAV-CSS. [4] Optimize EE and user outages. Multi-Agent Reinforcement Learning.…”

Section: Research Topic On Uav-based Sensing Modeling Approach Refere...mentioning

confidence: 99%

“…Furthermore, the high altitude of UAVs leads to line of sight (LOS) channels with the ground nodes, in contrast to the terrestrial channels that suffer from multipath fading, shadowing, and severe path losses [3]. As a result, various applications like military, telecommunication, surveillance, medical supplies delivery, and rescue operations weigh the UAVs as perfect enablers for seamless communications [4]. The UAVs are also expected to be the center of 6G wireless networks, which have gained significant attention from the research community and industry to support emerging mobile services [5].…”

Section: Introductionmentioning

confidence: 99%

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UAV aided virtual cooperative spectrum sensing for cognitive radio networks

Gul,

Kim,

Ali

et al. 2023

PLoS ONE

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Cooperative spectrum sensing (CSS) involves multiple secondary users (SUs) reporting primary user (PU) channel sensing states to the fusion center (FC). However, the high overheads associated with multi-user CSS impose power limitations that limit its usefulness in unmanned aerial vehicle (UAV) networks. To address this challenge, we propose a virtual CSS, where a single UAV conducts CSS while following a circular flight trajectory in the air. The novelty of our approach is presenting a working frame structure for the UAV flight, including sensing and data transmission periods with further division of the sensing time into mini-sensing slots. In the virtual CSS, UAV performs local sensing decisions in each mini-slot and accumulates them for a final decision. The proposed virtual CSS scheme exploits sequential decision fusion (SDF), which sequentially adds individual mini-slot decisions. Additionally, we leverage machine learning (ML), employing AdaBoost ensembling classifier (ENC), to inspect flight conditions and reconfigure mini-slot periods dynamically for both traditional decision fusion (TDF) and our proposed SDF schemes. Furthermore, we identify an optimal decision threshold (ODT) for the proposed SDF, enabling the comparison of sequential results with an adjustable threshold through majority voting. This novel approach results in energy efficiency and improved throughput for virtual CSS using SDF, surpassing the performance of TDF, which relies on collecting entire mini-slot reports for its final decision. Simulation results demonstrate the effectiveness of the proposed SDF following the ENCODT (SDF-ENCODT) scheme compared to existing techniques from the literature. We explore varying levels of UAV flight velocities, moving radius, detection probability demand, and channel signal-to-noise ratio (SNR), reinforcing the significance of our contribution. Our research highlights the motivation to address spectrum scarcity in UAV communication by proposing an innovative virtual CSS scheme based on SDF. The proposed approach enhances spectrum utilization, overcomes power limitations, and substantially improves CSS for UAV networks.

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“…Intra-frame structure-based cognitive UAV-CSS. [4] Optimize EE and user outages. Multi-Agent Reinforcement Learning.…”

Section: Research Topic On Uav-based Sensing Modeling Approach Refere...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UAV aided virtual cooperative spectrum sensing for cognitive radio networks

Gul,

Kim,

Ali

et al. 2023

PLoS ONE

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“…In Reference 25, Y. Xin and L. Lai discussed how to design quickest sequential scanning algorithms for multiband systems and quickly identify free channels. In References 30,31, J. Wu et al formulated a soft truncation terminating sequential probability ratio test (SPRT) process in CUAVNs to achieve quick CSS under the sensing delay limit. Following applications of the quickest/sequential detection in References 15,16,24,25,30,31, the CSS problem of CUAVNs still poses many research challenges, especially in the aspect of the cooperative performance and cooperative efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…In References 30,31, J. Wu et al formulated a soft truncation terminating sequential probability ratio test (SPRT) process in CUAVNs to achieve quick CSS under the sensing delay limit. Following applications of the quickest/sequential detection in References 15,16,24,25,30,31, the CSS problem of CUAVNs still poses many research challenges, especially in the aspect of the cooperative performance and cooperative efficiency. Given the need for low latency in UAV communications, this requires a reduction in the number of sensing samples to improve the cooperative efficiency, but this also means a reduction in the cooperative performance.…”

Section: Introductionmentioning

confidence: 99%

Optimal sequential decision rule for cooperative spectrum sensing of CUAVNs

Bao

2022

Trans Emerging Tel Tech

Self Cite

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Cognitive radio (CR) technology is beneficial for unmanned aerial vehicles (UAVs) to access the spectrum resources needed for communication. Then, in order to integrate CR technology into UAVs without causing excessive interference to the primary users (PUs), cooperative spectrum sensing (CSS) is used as a key function of CR technology to detect the PUs for the UAV's available spectrum. However, the flexible locations of UAVs may significantly degrade the performance and efficiency of CSS within them. Hence, on the basis of a cognitive UAV networks (CUAVN) framework, we propose an internal cooperation paradigm to achieve the cooperative gain of CSS within an UAV. Furthermore, we convert the decision rule as an optimal stopping problem to optimize the decision rule for CSS. The optimization problem is carried out by the error probability regarding the PU presence of the phenomenon and the stopping time required to reach a global decision about the PU status. By optimizing the error probability respectively under the condition of a fixed sensing time, the optimization problem is transformed into an optimal stopping problem and solved by Markov stopping theory. Following this, we further determine a pair of thresholds in the optimal sequential decision rule from the minimal cost function. At last, numerical simulation results verify the correctness and effectiveness of the optimal sequential detection rule, and corroborate that in contrast to the external cooperative paradigm, the superiority of the internal cooperative paradigm under various network environments and parameters, in terms of the CSS performance and the stopping time.

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