Convolutional Neural Network for Cooperative Spectrum Sensing with Spatio-Temporal Dataset

Shachi, P.; Sudhindra, K. R.; Suma, Marco

doi:10.1109/aisp48273.2020.9073175

Cited by 12 publications

(10 citation statements)

References 13 publications

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“…RL [163] 2018 Classify input signal as primary user or secondary user. DAENN and SVM [164] 2020 Considered Spectrum sensing as classification problem. Used cooperative spectrum sensing scenario to classify the sensed signal from primary user as available or busy.…”

Section: Refmentioning

confidence: 99%

“…With this method, they achieved up to 95% spectrum sensing accuracy. In [164], the authors applied a spatio-temporal system model of cooperative spectrum sensing to classify sensed signal and detect primary users based on a convolutional neural network (CNN). The authors examined different scenarios depending on how the primary users were placed and showed that the proposed model performed well with different level of noise.…”

Section: A Spectrum Sensingmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey of Spectrum Sharing Schemes from a Standardization and Implementation Perspective

Parvini¹,

Zarif²,

Nouruzi³

et al. 2022

Preprint

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Section: Refmentioning

confidence: 99%

Section: A Spectrum Sensingmentioning

confidence: 99%

A Comprehensive Survey of Spectrum Sharing Schemes from a Standardization and Implementation Perspective

Parvini¹,

Zarif²,

Nouruzi³

et al. 2022

Preprint

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“…Lastly, in [24], they proposed a CSS approach generating a dataset with 25 UU and using a CNN in two scenarios. The data were generated taking into consideration the inphase and quadrature components and frame size, so the data have the shape 25 × 128 × 2 for all the scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…The CSS consists in combining the wideband sensing information of multiple UU in order to increase the probability of correct identification of the LU [24][25][26]. Along with CSS, approaches involving deep learning networks have shown promise to identify LU in the spectrum, and recurrent neural networks (RNN) and convolutional neural networks (CNN) are recently the most proposed classification methods for SS [27].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in [28], they proposed a CSS approach using an energy detection technique and a supervised CNN to determine the presence, or not, of LU. In [24], they proposed a CNN to identify the presence of LU based in a spatial-temporal dataset. Furthermore, in [29], they proposed a Long Short-Term Memory (LSTM) based deep learning model to predict the multi-channel conditions.…”

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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Cooperative Spectrum Sensing with Distributed/Centralized Relay Selection

Halima

Boujemâa²

2020

Wireless Pers Commun

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Convolutional Neural Network for Cooperative Spectrum Sensing with Spatio-Temporal Dataset

Cited by 12 publications

References 13 publications

A Comprehensive Survey of Spectrum Sharing Schemes from a Standardization and Implementation Perspective

A Comprehensive Survey of Spectrum Sharing Schemes from a Standardization and Implementation Perspective

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

Cooperative Spectrum Sensing with Distributed/Centralized Relay Selection

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