Narrowband Spectrum Sensing in Cognitive Radio Detection Methodologies

Jain, Praneeth P.; Pawar, Pradeep R.; Patil, Prajwal; Pradhan, Devasis

doi:10.26438/ijcse/v7i11.105113

Cited by 6 publications

(5 citation statements)

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“…is the additive white Gaussian noise (AWGN) whose noise power density is N 0 , mean zero and standard deviation σ = B W 10 N 0 10 . Being η the proportion of power leaked to adjacent bands, then B P are the bands occupied by the LU and B A are the bands affected by the leaked power of the LU.…”

Section: Signal Generationmentioning

confidence: 99%

“…There are two main SS approaches, the narrowband and wideband sensing approaches [8]. In the narrowband approach, each frequency channel is analyzed at a time, differently from the wideband approach, where many channels are analyzed sequentially or simultaneously [9], although the sequential method requires a longer time and more consumption of energy, which is not ideal for real-time communication systems [10].…”

Section: Introductionmentioning

confidence: 99%

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Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest Classifier

Valadao

Amoedo

Costa

et al. 2021

Sensors

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Some bands in the frequency spectrum have become overloaded and others underutilized due to the considerable increase in demand and user allocation policy. Cognitive radio applies detection techniques to dynamically allocate unlicensed users. Cooperative spectrum sensing is currently showing promising results. Therefore, in this work, we propose a cooperative spectrum detection system based on a residual neural network architecture combined with feature extractor and random forest classifier. The objective of this paper is to propose a cooperative spectrum sensing approach that can achieve high accuracy in higher levels of noise power density with less unlicensed users cooperating in the system. Therefore, we propose to extract features of the sensing information of each unlicensed user, then we use a random forest to classify if there is a presence of a licensed user in each band analyzed by the unlicensed user. Then, information from several unlicensed users are shared to a fusion center, where the decision about the presence or absence of a licensed user is accomplished by a model trained by a residual neural network. In our work, we achieved a high level of accuracy even when the noise power density is high, which means that our proposed approach is able to recognize the presence of a licensed user in 98% of the cases when the evaluated channel suffers a high level of noise power density (−134 dBm/Hz). This result was achieved with the cooperation of 10 unlicensed users.

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Section: Signal Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest Classifier

Valadao

Amoedo

Costa

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…There are also a lot of ways to sense the spectrum in cognitive radio, including energy detection, matched filter detection, cyclostationary detection (CD), and using more spectrum. Because it is a common signal, we are employing an energy detection strategy here [2][3][4]. Due to its extremely simple implementation, the spectrum sensing methods.…”

Section: Introductionmentioning

confidence: 99%

4G/VoLTE- Spectrum Sensing using Cyclostationary Detection (CS-SS) Method: A Review

Kumari¹,

Shrishti²,

Jain³

2023

Middle East Res J Engr Technol

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Artificial Neural Networks (ANNs), Deep Learning, and AI Deep learning is a subset of machine learning. In order to create pictures with a greater resolution, a high-resolution GAN combines a deep network with an opponent network. An approach to generative modelling that uses deep learning techniques like convolutional neural networks is known as generative adversarial networks, or GAN. In the Super-Resolution Generative Adversarial Network (SRGAN), a Generative Adversarial Network (GAN) may transform low-resolution images into super resolution images that are more finely detailed and of higher quality. In the past, CNNs were used to produce incredibly precise and detailed images. However, they could have trouble recalling specifics and usually draw hazy visuals which can be overcome by SRGAN.

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“…Spectrum sensing is the process through which the status of the spectrum is checked. If only one frequency band is taken into consideration it is called as narrowband spectrum sensing [7] whereas if multiple frequency bands are taken into consideration, it is called as wideband spectrum sensing. Matched filter detection [8], cyclo-stationary feature detection [9], covariance based detection [10], machine learning based spectrum sensing [11], energy detection [12] etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Low Complexity High Resolution Spectrum Hole Detection Technique for Cognitive Radio

Sajeevu¹,

Sakthivel²

2022

Preprint

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Cognitive radio is a potential solution to meet the upcoming spectrum crunch issue. In a cognitive radio, spectrum holes can be identified using spectrum sensing techniques. A high resolution spectrum hole detection can ensure even the smallest inactive portion in the spectrum is efficiently utilized. In this paper, a spectrum hole detection technique is proposed in which coarse sensing is done initially so as to detect occupied channels simultaneously. Spectrum holes in the occupied band can be efficiently detected using a fine sensing method. A two stage Frequency Response Masking (FRM) filter sandwiched between two Pascal structure based sampling rate converters results in arbitrary variation of bandwidth. This arbitrary variation of bandwidth can be utilized for fine sensing the spectrum such that the spectrum holes can be detected with high resolution. In the proposed method, high resolution in spectrum hole detection can be achieved without increasing the hardware complexity of the design. The hardware complexity of the proposed method is compared with the state of the art and is found to be significantly less.

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Narrowband Spectrum Sensing in Cognitive Radio Detection Methodologies

Cited by 6 publications

References 0 publications

Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest Classifier

Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest Classifier

4G/VoLTE- Spectrum Sensing using Cyclostationary Detection (CS-SS) Method: A Review

A Novel Low Complexity High Resolution Spectrum Hole Detection Technique for Cognitive Radio

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