Performance evaluation of energy detection based on non-cooperative spectrum sensing in cognitive radio network

Yawada, Prince Semba; Wei, An Jian; Kiki, Mesmin Mbyamm

doi:10.1109/iccsnt.2015.7490914

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Primary and secondary nodes in cognitive radio networks use interweave to share the same spectrum [9]. Interference-based, non-cooperative, and cooperative detection are the three sorts of techniques for spectrum sensing [10], [11]. Several spectrum sensing methods and solutions have been developed throughout the last decade, each with operational needs, benefits, and drawbacks [12].…”

Section: Introductionmentioning

confidence: 99%

Covariance absolute values spectrum sensing method based on two adaptive thresholds

Hashim

Ziboon

Abdulsatar

2023

IJEECS

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Cognitive radio is a modern wireless communication methodology that deals with the issue of spectrum untapped. Cognitive users can continually perceive the licensed spectrum to hunt for unoccupied spots. The essential technology in cognitive radio (CR) for primary user detection is spectrum sensing. Due to signal to noise ratio (SNR), noise uncertainty in spectrum sensing would make the detection unreliable. In this paper, the two adaptive thresholds based on_covariance absolute values (TATCAV) are proposed to increase detection performance in the presence of noise uncertainty. According to the computer simulations using MATLAB 2021b, the value of the probability of detection is Pd=98.1% Compared with the results of two thresholds based on_covariance absolute values (TTCAV) Pd= 95.3% at SNR=-18, noise uncertainty Nu=1.761 dB, and using quadrature amplitude modulation (QAM). And the error rate for the proposed approach is Pe=12.1% under the same circumstances. The proposed approach results, according to the simulations, are considerably better than the results of the fixed two-threshold approach.

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Section: Introductionmentioning

confidence: 99%

Covariance absolute values spectrum sensing method based on two adaptive thresholds

Hashim

Ziboon

Abdulsatar

2023

IJEECS

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“…Because of ED's low computational and implementation difficulties, it is the most widely used technique for spectrum sensing as mentioned in [1]. The drawback of energy detection ^R eceived: Reviewed: Accepted:…”

Section: Introductionmentioning

confidence: 99%

Enhanced Energy Detectors Utilizing Wavelet and RLS De-Noising Filters in Cognitive Radio

Ishiba¹,

Yousef²,

Tarrad³

2019

IJAST

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Spectrum resources are limited due to the fast developing technology in wireless communications. Various techniques, for instance, tackled this problem by giving permissions to unlicensed users to utilize the various licensed bands. Spectrum sensing is one of the most utilized technique in cognitive radio system, which has a reliable technique called energy detection. It has reduced the computational and complexities of usage. In order to differentiate between the primary and secondary users in low signal-to-noise ratio (SNR), noise interference is eliminated using denoising filters. In this paper, a new energy detection technique for spectrum sensing is developed. Besides that, a comparison between common de-noising filters is introduced, which are Recursive Least Square (RLS), 1-D and 2-D wavelet de-noising filters. The performance of the wavelet packet transform algorithm is analyzed under many Signal to Noise Ratios, different number of samples, probability of false alarm levels, and number of samples. Moreover, the technique is analyzed for different level of decomposition and different wavelet families. Simulation results revealed that utilizing RLS de-noising filter outperforms the technique used by wavelet de-noising filters under all analyzed circumstances.

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“…Spectrum sensing (SS) offers the Secondary Users (SU) an access to these unlicensed bands without making an interference with the Primary Users (PU). Energy detection is the most widely used technique for spectrum sensing because of its low computational and implementation complexities as mentioned in [1]. On the other hand, energy detection suffers of the poor performance under low SNR scenarios as presented in [2].…”

Section: Introductionmentioning

confidence: 99%

Efficient spectrum sensing technique based on energy detector, compressive sensing, and de-noising techniques

Hussein

Kasem

Adel

2016

IJET

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Highdata rate cognitive radio (CR) systems require high speed Analog-to-Digital Converters (ADC). This requirement imposes many restrictions on the realization of the CR systems. The necessity of high sampling rate can be significantly alleviated by utilizing analog to information converter (AIC). AIC is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal has a sparse representation in some dictionary, which can be recovered from a small number of linear projections of that signal. This paper proposes an efficient spectrum sensing technique based on energy detection, compression sensing, and de-noising techniques. De-noising filters are utilized to enhance the traditional Energy Detector performance through Signal-to-Noise (SNR) boosting. On the other hand, the ordinary sampling provides an ideal performance at a given conditions. A near optimal performance can be achieved by applying compression sensing. Compression sensing allows signal to be sampled at sampling rates much lower than the Nyquist rate. The system performance and ADC speed can be easily controlled by adjusting the compression ratio. In addition, a proposed energy detector technique is introduced by using an optimum compression ratio. The optimum compression ratio is determined using a Genetic Algorithm (GA) optimization tool. Simulation results revealed that the proposed techniques enhanced system performance.

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Performance evaluation of energy detection based on non-cooperative spectrum sensing in cognitive radio network

Cited by 8 publications

References 15 publications

Covariance absolute values spectrum sensing method based on two adaptive thresholds

Covariance absolute values spectrum sensing method based on two adaptive thresholds

Enhanced Energy Detectors Utilizing Wavelet and RLS De-Noising Filters in Cognitive Radio

Efficient spectrum sensing technique based on energy detector, compressive sensing, and de-noising techniques

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