Identification of spectrum holes using energy detector based spectrum sensing

Chaudhary, Neha; Mahajan, Rashima

doi:10.1007/s41870-021-00662-6

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…2, June 2023: 966-973 968 primary user over the sample, are the two types of parameters which are present in the Neyman pearson. Probability-of-detection is computed in (1). Additive white gaussian noise (AWGN) is used in this research, this is the type of random noise and it has widespread range of frequency over the channel.…”

Section: Methodsmentioning

confidence: 99%

“…Depending upon the range of frequencies, sensing process has to be decided and according to their utilization sharing and mobility process will be performed. Further, management process will be considered for better usage of spectrum [1], [2]. Among four basic functions of cognitive radio, more emphasis is placed on sensing process which includes the detection of unused bands and then used by the secondary users.…”

Section: Introductionmentioning

confidence: 99%

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Spectrum sensing using 16-QAM and 32-QAM modulation techniques at different signal-to-noise ratio: a performance analysis

Chaudhary

Mahajan

2023

IJ-AI

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<p>Spectrum sensing techniques are implemented for effective use of spectrum resources in the cognitive-radio. In this research work attempt has been made for the performance of energy detector with cooperative spectrum sensing using double dynamic threshold on the MATLAB software. Additive white gaussian noise is used and the frequency range between 54 MHz to 862 MHz is considered with wideband. Findings in receiver operative curve have been observed to analyze probability-of-detection (𝑃𝑑) under different values of probability-of-false alarm (𝑃𝑓) condition and diverse ranges of signal to noise ratio with different number of samples of input signal. Presence and absence of primary user has been marked by using a hypothetical model based on Neyman pearson approach. From the results, it has been observed that more the number of sample values, better is the probability-of-detection (𝑃𝑑) value as achieved for 32-QAM signal as compared to 16-QAM signal. Also, better results have been witnessed at -9db signal to noise ratio value as compared to -15db and -20db. This work provides almost 10% of enhancement in the probability of detection at -9db signal to noise ratio for 16-QAM modulated signal as compared to the existing model where the implementation of energy detector spectrum sensing technique through simulink model.</p>

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectrum sensing using 16-QAM and 32-QAM modulation techniques at different signal-to-noise ratio: a performance analysis

Chaudhary

Mahajan

2023

IJ-AI

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“…Several research studies have focused on spectrum sensing using a wide variety of white space detectors to tackle the tradeoff between sensing time and data transmission. Existing detectors can be categorized as interference-based, cooperative, energy, cyclo-stationary, matching filter, and blind [7], [8]. In addition, hybrid detection techniques also exist, such as the delta detector [3], which combines energy and blind detection to enhance detection accuracy and reduce the time needed for sensing.…”

Section: Introductionmentioning

confidence: 99%

Spectral–Temporal Model for Opportunistic Spectrum Access in Cognitive Radio Networks

Galib,

Younis,

Stevens

2024

IEEE Trans. Cogn. Commun. Netw.

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“…Probability of detection (𝑃 𝑑 ) indicates actual presence of primary or licensed user (PU) and probability-of-false-alarm (𝑃 𝑓 ) is the indicator of false appearance of PU. Conventional energy detector spectrum sensing techniques involves implementation using single threshold method and its performance get deteriorated in low SNR [2]. Spectrum sensing detection is reported to be Int J Artif Intell ISSN: 2252-8938  improved with double threshold method followed by cooperative spectrum sensing where each cognitive radio takes its own decision for detection of primary user [3].…”

Section: Introductionmentioning

confidence: 99%

“…The two-hypothesis indicators H0 and H1 are defined for absence or existence of the primary user [5], [6]. Two hypothesis shown in ( 1) and (2).…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of cooperative spectrum sensing using double dynamic threshold

Chaudhary

Mahajan²

2023

IJ-AI

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<p>Increased use of wireless technologies and in turn more utilization of available spectrum is subsequently leading to the increasing demand for wireless spectrum. This research work incorporates spectrum sensing detection consisting of a double dynamic threshold followed by cooperative type spectrum sensing. The performance has been analyzed using two modulation schemes, quadrature-amplitude-modulation (QAM) & binary-phase-shift-keying (BPSK). Improved probability of detection has been witnessed using the double dynamic threshold where a comparison of average values of local decision (LD) and the observed value of energy (EO) has been considered instead of using direct values of local decisions and energy. Further, the probability-of-detection ( ) is found to be better with QAM as compared to the BPSK. From the results, it has been observed that the detection of primary users is also affected by the number of samples. The simulation environment considered for this work is MATLAB and the performance of cooperative spectrum sensing for 500 and 1000 samples with -9db and -12 SNR by considering different false alarm values i. e 0.1,0.3 and 0.5 has been analyzed. The further scope shall be to enhance the primary user detection by considering different QAM schemes and different SNRs.</p>

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Identification of spectrum holes using energy detector based spectrum sensing

Cited by 10 publications

References 31 publications

Spectrum sensing using 16-QAM and 32-QAM modulation techniques at different signal-to-noise ratio: a performance analysis

Spectrum sensing using 16-QAM and 32-QAM modulation techniques at different signal-to-noise ratio: a performance analysis

Spectral–Temporal Model for Opportunistic Spectrum Access in Cognitive Radio Networks

Performance analysis of cooperative spectrum sensing using double dynamic threshold

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