Centralized Cooperative Directional Spectrum Sensing for Cognitive Radio Networks

Na, Woongsoo; Yoon, Jongha; Cho, Sungrae; Griffith, David; Golmie, Nada

doi:10.1109/tmc.2017.2768509

Cited by 31 publications

(16 citation statements)

References 43 publications

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“…e target detection probability P d is usually preset to ensure the sensing performance; then the false alarm probability can be obtained by combining (11) and (12):…”

Section: System Modelmentioning

confidence: 99%

“…In "OR" rule, if any local sensing result has shown the presence of the PU, then the final decision is that the PU is present. e false alarm probability P f (λ, θ 0 ) and the detection probability P d (λ, θ 0 ) of each mini sensing radian have been given in (11) and (12). e expression of cooperative false alarm probability and cooperative detection probability of multiradian are derived as follows:…”

Section: Optimization Of Se In Multiradian Cssmentioning

confidence: 99%

“…However, the influence of channel fading and noise interference will result in poor detection performance. erefore, cooperative spectrum sensing (CSS) is proposed to improve the sensing performance [12]. In CSS, multiple local sensing results are comprehensively analysed to obtain the final decision by the fusion centre (FC) using "logic-AND" rule, "logic-OR" rule, and "n-out-of-K" rule [13].…”

Section: Introductionmentioning

confidence: 99%

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Spectrum Efficiency Optimization for UAV-Based Cognitive Radio Network

Zhang

et al. 2020

Mathematical Problems in Engineering

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Unmanned aerial vehicle- (UAV-) assisted communication has great potential to provide on-demand wireless services and improve the outdoor link throughput. In this paper, a UAV-based cognitive radio network (CRN) is investigated in which the UAV works as a secondary user (SU). Considering the overlay spectrum sensing mode, the UAV can operate on the licensed spectrum bands of primary user (PU) only when PU is idle. In each working frame structure, both sensing time slot and transmission time slot are analysed in radians. Specifically, our objective is to maximize the spectrum efficiency (SE) of the UAV by jointly optimizing the sensing radian and the number of radians. For the single-radian and multiradian schemes, the dichotomy and alternative iterative optimization (AIO) algorithm are proposed to solve the SE optimization problem. Simulation results show that the proposed multiradian cooperative spectrum sensing (CSS) scheme can achieve better performance on ensuring the quality-of-service (QoS) of the PU, and it can significantly enhance the SE of the UAV especially in the severe channel environments.

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“…e target detection probability P d is usually preset to ensure the sensing performance; then the false alarm probability can be obtained by combining (11) and (12):…”

Section: System Modelmentioning

confidence: 99%

Section: Optimization Of Se In Multiradian Cssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectrum Efficiency Optimization for UAV-Based Cognitive Radio Network

Zhang

et al. 2020

Mathematical Problems in Engineering

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“…Woongsoo et al, [20] proposed the directional antennas deployment in CRNs with centralized cooperative directional sensing scheme. This scheme outperforms other nonoptimized schemes.…”

Section: Fig 7 Spectrum Managementmentioning

confidence: 99%

A Comprehensive Review on Spectrum Management, Security and Energy-Efficient Cognitive Radio Networks

Manjula¹,

Venugopal²,

Gudihatti³

2019

IJCA

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The explosive growth of mobile devices and the rapid increase in wireless services necessitate an improvement in communication techniques. This technology can boost spectrum utilization as well as meet the massive connectivity requirement to draw attention from academia and industry. Cognitive Radio Network (CRN) is an evident optimal solution in alleviating the challenges of spectrum unused potentially in wireless networks. The Secondary Users are allowed intelligently to access the sections of spectrum unused (spectrum holes) by the authorized Primary Users (PUs). The objective of this paper is to analyze channel throughput, delay, carrier aggregations, energy efficiency, and security challenges and provide research ideas to enhance the overall performance of the CRN. This review paper completely investigated the potential management of spectrum techniques, essentially in spectrum sensing, spectrum allocation, spectrum sharing, and spectrum handoff in CRN. In addition, we study the energy efficient routing techniques like network optimization frameworks in cooperative networks, protocols and different types of attack issues, approaches for handling the secure communication in CRNs. The state of art review, which deeply examines the Cognitive Radio Network providing an idea of important technology and challenges are addressed. The open research challenges related to spectrum management, energy efficient routing and security are discussed.

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“…This is an infrastructure based approach where spectrum sensing results are sent to the FC where they are processed and analyzed. The FC derives a global decision regarding the status of the sensed channels and takes a single decision that is relayed back to SUs [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Spectrum Occupancy Reconstruction in Distributed Cognitive Radio Networks Using Deep Learning

Hlophe

Maharaj

2019

IEEE Access

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Spectrum occupancy reconstruction is an important issue often encountered in collaborative spectrum sensing in distributed cognitive radio networks (CRNs). This issue arises when the spectrum sensing data that are collaborated by secondary users have gaps of missing entries. Many data imputation techniques, such as matrix completion techniques, have shown great promise in dealing with missing spectrum sensing observations by reconstructing the spectrum occupancy data matrix. However, matrix completion approaches achieve lower reconstruction resolution due to the use of standard singular value decomposition (SVD), which is designed for more general matrices. In this paper, we consider the problem of spectrum occupancy reconstruction where the spectrum sensing results across the CRN are represented as a plenary grid on a Markov random field. We formulate the problem as a magnetic excitation state recovery problem, and the stochastic gradient descent (SGD) method is applied to solve the matrix factorization. SGD is able to learn and impute the missing values with a low reconstruction error compared with SVD. The graphical and numerical results show that the SGD algorithm competes favorably SVD in the matrix factorization by taking advantage of correlations in multiple dimensions. INDEX TERMS Cognitive radio networks, Ising model, matrix factorization, Metropolis-Hastings algorithm, missing values, stochastic gradient descent.

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Centralized Cooperative Directional Spectrum Sensing for Cognitive Radio Networks

Cited by 31 publications

References 43 publications

Spectrum Efficiency Optimization for UAV-Based Cognitive Radio Network

Spectrum Efficiency Optimization for UAV-Based Cognitive Radio Network

A Comprehensive Review on Spectrum Management, Security and Energy-Efficient Cognitive Radio Networks

Spectrum Occupancy Reconstruction in Distributed Cognitive Radio Networks Using Deep Learning

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