Spectrum Sensing Optimization and Performance Enhancement of Cognitive Radio Networks

Emara, Mostafa; Ali, Hanaa S.; Khamis, S.; El‐Samie, Fathi E. Abd

doi:10.1007/s11277-015-2962-5

Cited by 19 publications

(23 citation statements)

References 35 publications

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“…The AND rule, OR‐rule, and Majority rule are special scenarios of the K out of M rule with K = 1, K = M, and K = M/2, correspondingly . This CSS scenario is investigated in the recent work …”

Section: Proposed Methodologymentioning

confidence: 99%

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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: Proposed Methodologymentioning

confidence: 99%

“…Two SS techniques are concerned by Emara et al for CR networks: the first technique depends upon the ED and the second technique depends upon the multitaper spectral estimation. By utilizing the K out of M fusion rule, energy detector (ED) is analysed elaborately for single user as well as cooperative multiple users.…”

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 the CR networks, the CR checks the spectrum allocated to the PU and when it detects the absence of the PU transmission, it starts the data transmission to its receiver. The received samples at the CU receiver are [3]:…”

Section: Energy Detection Techniquementioning

confidence: 99%

“…Energy detection is a simple technique for spectrum sensing and is one of the most commonly employed spectrum sensing schemes. It does not require any prior knowledge about the primary users' (PUs) signals [3]. Two scenarios of spectrum sensing are usually used, a non-cooperative scenario and cooperative scenario (CSS).…”

Section: Introductionmentioning

confidence: 99%

“…Two scenarios of spectrum sensing are usually used, a non-cooperative scenario and cooperative scenario (CSS). In the noncooperative scenario, a single user is used to make the sensing, while in the cooperative scenario, multiple cognitive users sense in cooperation to reduce the effect of fading [3]. In CSS, during the transmission stage, the local sensing results are reported to a central entity, called the fusion centre (FC).…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Energy Consumption in Spectrum Sensing Cognitive Radio Networks Using an Efficient Two Stage Sensing Method

Abdullah

Abed

2017

Acta Polytech

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Abstract. Cognitive radio (CR) is a wireless technology developed to improve the usage in the spectrum frequency. Energy consumption is considered as a big problem in this technology, especially during a spectrum sensing. In this paper, we propose an algorithm to improve the energy consumption during the spectrum sensing. The theoretical analysis to calculate the amount of energy consumption, using the proposed method during sensing stage as well as the transmission stage during transmitting a local decision to the fusion center FC, are derived. The proposed algorithm is using energy detection technique to detect the presence or absence of the primary user (PU). The proposed algorithm consists of two stages: the coarse sensing stage and fine sensing stage. In the coarse sensing stage, all the channels in the band are sensed shortly and the channel that have maximum (or minimum) energy is identified to make a dense fine sensing for confirming the presence of the PU signal (or hole). The performance of the proposed algorithm is evaluated in two scenarios: non-cooperative, and cooperative in both the AWGN and Rayleigh fading channels. The simulation results show that the proposed method improves the energy consumption by about 40 % at a low SNR values, when compared with the traditional methods based on a single sensing stage and more advanced method based on censoring and sequential censoring algorithms.

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A Novel Spectrum Sensing Method Based on Tri-Stable Stochastic Resonance and Quantum Particle Swarm Optimization

Huang

Yang

2017

Wireless Pers Commun

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Spectrum Sensing Optimization and Performance Enhancement of Cognitive Radio Networks

Cited by 19 publications

References 35 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)

Improvement of Energy Consumption in Spectrum Sensing Cognitive Radio Networks Using an Efficient Two Stage Sensing Method

A Novel Spectrum Sensing Method Based on Tri-Stable Stochastic Resonance and Quantum Particle Swarm Optimization

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