Markov Model-Based Energy Efficiency Spectrum Sensing in Cognitive Radio Sensor Networks

Jiao, Yan; Joe, Inwhee

doi:10.1155/2016/7695278

Cited by 15 publications

(8 citation statements)

References 24 publications

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“…Due to SS, Jiao and Joe work concentrates much on energy consumption. Moreover, it provides an adaptive spectrum sensing time interval strategy, where the SUs could adapt the subsequent SS time interval based on the present SS outcomes.…”

Section: Literature Reviewmentioning

confidence: 99%

BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

Babu¹,

Venkataramanan²

2020

Int J Communication

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The vacant licensed spectrum can be employed by the secondary users in cognitive radio networks. Nevertheless, this process of identification is frequently agreed with shadowing, multipath fading and receiver uncertainty problems. The primary contributions of the spectrum sensing techniques adopted in this work mainly concentrate on energy detection for spectrum sensing as well as to recognize the optimal spectral estimation according to the multitaper spectral estimation. The algorithm proposed in this work is based on bivariate Lévy-stable bat algorithm (BLSBA), energy detector (ED), and with the help of K out of M fusion rule; it is analysed for the single user as well as cooperative multiple users. In the BLSBA algorithm, a modified search equation with more helpful information from the search experiences is brought-in to create an optimal energy solution and bivariate Lévy-stable random walk is associated with BLSBA to remove the trapping process into local optima. The energy detection is defined numerically from this optimal detection. At last, a multi-taper spectral estimator (MSE) is proposed to cognitive radio detection for a huge network. The simulation results in both cases are computed and checked with the help of a BLSBA optimizer. For an individual secondary user scenario, the advancement of MSE to the ED is broadly described. Experimental result indicates that the objective false alarm likelihood is minimized and the demanded signal-to-noise ratio is accomplished to the extent. INDEX TERMSbivariate Lévy-stable bat algorithm (BLSBA)cognitive radio networks (CRNs); energy detector (ED)multitaper spectral estimator (MSE)optimizationspectrum sensing

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Section: Literature Reviewmentioning

confidence: 99%

BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

Babu¹,

Venkataramanan²

2020

Int J Communication

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“…In [29] the authors addressed the energy efficiency issues of sensor network when incorporating cognitive radio. The channel sensing of cognitive radio will induce energy overhead head for resource constrained sensor device.…”

Section: Literature Surveymentioning

confidence: 99%

A Dynamic Spectrum Access Optimization Model for Cognitive Radio Wireless Sensor Network

Saroja¹,

Ragha²,

Sharma³

2017

IJCT

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The availability of low cost and tiny sensor devices have resulted in increased adoption of wireless sensor network (WSN) in various industries and organization. The WSN is expected to play a significant role in future internet based application services. WSN has been adopted in healthcare, disaster management, environment monitoring and so on. The low-cost availability of smart devices has led to increased use of wireless devices such as Bluetooth, Wi-Fi etc. Therefore, cognitive radio network plays a significant role in handling spectrum efficiently. The emerging internet access technology such as 4G and 5G network which is expected to come in near future is going to make cognitive spectrum access more challenging. The existing cognitive radio based WSN is not efficient in utilizing spectrum. They induce high collision due to interference and improper channel state information. To address, this work present an efficient distributed opportunistic spectrum access for wireless sensor network. The channel availability of likelihood distribution is computed using continuous-time Markov chain considering primary transmitting users temporal channel usage channel pattern and spatial distribution. The simulation outcome shows the proposed model achieves significant performance improvement over existing model. The proposed model improves the overall spectrum efficiency of cognitive radio wireless sensor network in terms of throughput, packet transmission and collision.

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“…A. Saad et al presented an investigative analysis of HMM based prediction in Reference 23. Y. Jiao and I. Joe in Reference 33 used HMM to reduce total energy consumption in CR sensor networks, while A. Saad et al in Reference 34 accurately predicted spectrum occupancy of industrial environments. In Reference 28, L. Di et al proposed a spectrum sensing algorithm based on hidden semi‐Markov model (HSMM) to enhance the spectrum sensing performance and work normally in a lower signal‐noise‐ratio (SNR) environment.…”

Section: Introductionmentioning

confidence: 99%

Optimal sequential decision rule for cooperative spectrum sensing of CUAVNs

Bao

2022

Trans Emerging Tel Tech

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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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Markov Model-Based Energy Efficiency Spectrum Sensing in Cognitive Radio Sensor Networks

Cited by 15 publications

References 24 publications

BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

A Dynamic Spectrum Access Optimization Model for Cognitive Radio Wireless Sensor Network

Optimal sequential decision rule for cooperative spectrum sensing of CUAVNs

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