Performance Evaluation of Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Network

Jaglan, Reena Rathee; Mustafa, Rashid; Sarowa, Sandeep; Agrawal, Sunil

doi:10.1007/978-3-319-30927-9_58

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…These methods are transmitter detection-based approaches and include Energy Detector, matched filter detector, cyclo-stationary detector, and entropy detector [?]. For its ease of use, energy detector has gained considerable popularity among other alternatives [5]. Unfortunately, problems with shadowing, multipath fading, and noise uncertainty cannot be overcome by using single-user spectrum sensing methods.…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Network Based Spectrum Sensing in Wireless Regional Area Network

Jain,

Yadav,

Kumar

et al. 2024

IEEE Access

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In recent development and improvement of wireless communication system, the cognitive radio (CR) is a potential approach to utilize spectrum efficiently. Spectrum sensing technique arguably is the most significant component of cognitive radio. Cooperative spectrum sensing (CSS) is utilized to improve the detection performance of the system. Several fusion strategies of decision making are presented for sensing the primary user but they do not perform well under low signal to noise ratio (SNR) conditions. This paper proposes artificial neural network (ANN) based CSS under Rayleigh multipath fading channel in IEEE 802.22 wireless regional area network (WRAN). We implemented an ANN in the fusion centre. First, the energy of the received signal is calculated using discrete wavelet packet transform (DWPT). Then, calculated energy, SNR and false alarm probability are used jointly to make a data set of 2048 samples and are used to train Levenberg-Marquardt back propagation training algorithm-based feed-forward neural network (FFNN). Using this trained neural network, CSS in WRAN is simulated under Rayleigh multipath fading channel and results demonstrate the superiority of the proposed approach over traditional CSS with DWPT and Fast fourier transform (FFT) based energy detection schemes in low SNR environment.

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Section: Related Workmentioning

confidence: 99%

Artificial Neural Network Based Spectrum Sensing in Wireless Regional Area Network

Jain,

Yadav,

Kumar

et al. 2024

IEEE Access

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“…CSS has much significance as it fights with the shadowing multipath fading in addition to receiver uncertainty issues. Two significant parameters in SS were given by Jaglan et al, they are probability of detection as well as probability of false alarm. Greater detection probability indicates improved PU protection.…”

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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“…Performance of sensing will be degraded in the case of single user when the fading effect occurs. To overcome this issue cooperative spectrum sensing (CSS) has been proposed [1][2][3]. The current research has generally related on the fusion center (FC) based collaborative model in which the detecting hubs report their detecting results to the common base station through control channel employing soft or hard methodologies.…”

Section: Introductionmentioning

confidence: 99%

Two Stage Fusion in Cognitive Radio Networks: A Clustering Approach

Mustafa¹,

Jaglan²,

Agrawal³

2020

MMEP

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The cognitive radio (CR) has been recently proposed as an auspicious technology to effectively utilize the radio band to allow unlicensed users by allocating the spectrum dynamically on a non-interfering basis. The main challenge of the CR system is control channel overhead when the number of secondary users (SUs) becomes very large in cooperative spectrum sensing (CSS) scenario. To mitigate this problem, Cluster based CSS approach is useful to avoid the crowding on the control channel. Therefore, in this paper, neural network approach based on supervised learning algorithm is applied at the fusion center (FC) for proposed clustering technique. The simulation results prove that proposed clustering approach enhance the accuracy of the system. Also, it is more robust than other conventional schemes.

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Performance Evaluation of Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Network

Cited by 7 publications

References 11 publications

Artificial Neural Network Based Spectrum Sensing in Wireless Regional Area Network

Artificial Neural Network Based Spectrum Sensing in Wireless Regional Area Network

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

Two Stage Fusion in Cognitive Radio Networks: A Clustering Approach

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