A Novel High Speed Two Stage Detector for Spectrum Sensing

Geethu, S.; Narayanan, Gayathri

doi:10.1016/j.protcy.2012.10.082

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…The majority of recent research studies assume that one of the adopted stages is ED. In the other stage, fast Fourier transform (FFT), fuzzy logic, maximum minimum eigenvalue (MME), covariance absolute value (CAV) and waveform detection can be used before or after the ED 19–33 …”

Section: Related Workmentioning

confidence: 99%

“…Geethu and Narayanan 27 presented two‐stage spectrum sensing techniques. In the general case, they used two‐stage detectors.…”

Section: Related Workmentioning

confidence: 99%

“…The authors used the two stages for all received signals with different SNR s. In a modified method, the second stage of the CAV detector is activated only if the SNR is above a certain specified value. The summary of the previous work 19,21–23,26,27 is shown in Table 1. This table gives a detailed comparison of the previous works in terms of the SNR for the PU signals ( SNR p ), probability of detection P d , probability of false alarm P f , number of sensing samples N , and detection time.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive two‐stage spectrum sensing model using energy detection and wavelet denoising for cognitive radio systems

Fawzi

El‐Shafai

Abd‐Elnaby

et al. 2020

Int J Communication

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This paper presents an efficient adaptive two-stage spectrum sensing model for the cognitive radio (CR) systems. The proposed model combines two wellknown techniques: energy detection (ED) and wavelet denoising (WD). The ED technique is employed to identify the existence of the primary user (PU) signal in the case of high signal-to-noise ratio (SNR) by comparing the received signal with a threshold. In the case of low SNR, the ED technique alone cannot be adopted for decision making about the status of the PU due to the noise impact on the received signals. Hence, the stage of WD is exploited prior to the ED for reducing the noise effect and detecting the PU signal in the presence of noise. The detection performance of the proposed model is compared with those of the ED alone and the previous related two-stage spectrum sensing methods. Simulation results show that the proposed model improves the false alarm rate compared to the ED alone by 36% and 6%, respectively, at low SNRs of −15 and −20 dB. Also, the proposed model yields the smallest detection time compared to the previous two-stage spectrum sensing methods. The proposed model can be used to minimize the probability of false alarm, achieve a better probability of detection, and improve the sensing process in CR systems.

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Section: Related Workmentioning

confidence: 99%

“…Geethu and Narayanan 27 presented two‐stage spectrum sensing techniques. In the general case, they used two‐stage detectors.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Adaptive two‐stage spectrum sensing model using energy detection and wavelet denoising for cognitive radio systems

Fawzi

El‐Shafai

Abd‐Elnaby

et al. 2020

Int J Communication

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“…A novel high-speed two-stage detector is proposed in [8] that effectively decreased the sensing time by satisfying the required detection capabilities. Energy detector is used in the coarse sensing stage and if the measured energy is greater than threshold then it declares PU present, else it computes the SNR of device.…”

Section: Related Workmentioning

confidence: 99%

“…This approach can be very healthy for CR applications for disaster management; smart grid, and so on to get reliable sensing results with minimum sensing time. Many improved local sensing schemes are proposed in [5][6][7][8][9][10][11][12], including our own fuzzy logic-based and SNR-based adaptive spectrum sensing for improved local sensing. In the proposed scheme, channels with known PU waveform will be sensed by matched filter detection and rest of the channels by the detectors which do not need dedicated circuitry and prior knowledge of PU waveform.…”

Section: Introductionmentioning

confidence: 99%

I3S: Intelligent spectrum sensing scheme for cognitive radio networks

Ejaz

Hasan

Lee

et al. 2013

J Wireless Com Network

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Reliable spectrum sensing is one of the most crucial aspects for the successful deployment of cognitive radio (CR) technology. For CR, it is not possible to transmit on a licensed band and sense it simultaneously, therefore sensing must be interleaved with transmission. Spectrum sensing in CR is challenged by a number of uncertainties, which degrade the sensing performance and in turn require much more time to achieve the targeted sensing efficiency. Hence, the authors propose a spectrum sensing scheme which obtains reliable results with less mean detection time. First, the scheme determines a better matched filter, or a combination of energy and cyclostationary detectors based on the power and band of interest. In the combined energy and cyclostationary detector, an energy detector with a bi-threshold is used, and the cyclostationary detector is applied only if the energy of the signal lies between two thresholds. Second, sensing is performed by the selection choice resulting from the first step. To evaluate the scheme's performance, the results are compared with those where only an energy detector, matched filter, or cyclostationary detector are performed. The performance metrics are the probability of detection, probability of false alarm, and mean detection time.

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Convolutional neural network model for spectrum sensing in cognitive radio systems

El‐Shafai

Fawzi

Sedik

et al. 2022

Int J Communication

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Summary Cognitive radio (CR) has become an interesting research field that attracts researchers due to its increasing spectrum efficiency. Therefore, spectrum sensing (SS) is the essential function of cognitive radio systems. This paper presents an efficient SS model based on convolutional neural networks (CNNs). We use the spectrogram images of the received signals as the input to the CNN and use various images for signal and noise at different low primary‐user (PU) signal‐to‐noise ratios (SNRs) to train the network model. The model extracts the main features from the spectrogram images to represent signals and noise. Hence, this model can efficiently discriminate between signal and noise at different SNRs. The detection performance of the suggested model is compared with those of the traditional one‐stage, two‐stage SS methods, and different previous CNN models. The obtained outcomes demonstrate that the suggested model increases the detection accuracy more than those of the previous one‐stage SS methods by 17% at low SNRs of −20 dB and more than the previous two‐stage SS methods by 8% at low SNRs of −20 dB. In addition, it is demonstrated that the suggested model offers shorter sensing times than those of the two‐stage and one‐stage SS methods in the orders of 16.3, 16.6, 1.1, and 1.5 ms at SNRs of −20, −15, −10 and −5 dB, respectively. Furthermore, the proposed model improves the detection accuracy better than the different previously compared CNN models by 28% and 19% at low SNRs of −20 and −15 dB, respectively.

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A Novel High Speed Two Stage Detector for Spectrum Sensing

Cited by 12 publications

References 9 publications

Adaptive two‐stage spectrum sensing model using energy detection and wavelet denoising for cognitive radio systems

Adaptive two‐stage spectrum sensing model using energy detection and wavelet denoising for cognitive radio systems

I3S: Intelligent spectrum sensing scheme for cognitive radio networks

Convolutional neural network model for spectrum sensing in cognitive radio systems

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