A Study and Simulation of Spectrum Sensing Schemes for Cognitive Radio Networks

Bharathy, G. T.; Rajendran, V.; Tamilselvi, T.; Meena, M.

doi:10.1109/icsss49621.2020.9202296

Cited by 8 publications

(2 citation statements)

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“…A collaborative spectrum sensing for CR network is explained in [3]. Detailed analysis and comparison of various spectrum sensing techniques and have presented a scheme for coverage area restoration when the failure of nodes is presented in [4,5]. A novel GIID for cooperative cognitive networks is designed in [6].…”

Section: Related Workmentioning

confidence: 99%

Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Gnanasivam¹,

Bharathy²,

Rajendran³

et al. 2023

Computer Systems Science and Engineering

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Wireless Communication is a system for communicating information from one point to other, without utilizing any connections like wire, cable, or other physical medium. Cognitive Radio (CR) based systems and networks are a revolutionary new perception in wireless communications. Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum. Centralized Cooperative Spectrum Sensing (CSS) is a kind of spectrum sensing. Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix (SCM) of the received signal. Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector (PRIDe), Hadamard Ratio (HR) detector, Gini Index Detector (GID), etc. This paper presents the simulation and comparative performance analysis of PRIDe with various other detectors like GID, HR, Arithmetic to Geometric Mean (AGM), Volume-based Detector number 1 (VD1), Maximumto-Minimum Eigenvalue Detection (MMED), and Generalized Likelihood Ratio Test (GLRT) using the MATLAB software. The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.

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Section: Related Workmentioning

confidence: 99%

Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Gnanasivam¹,

Bharathy²,

Rajendran³

et al. 2023

Computer Systems Science and Engineering

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“…Tradition spectrum sensing methods use different signal features, including energy detection [4,5], pilot detection [6,7], cyclostationary detection [8,9], and so on. The problem is that these methods can't achieve good performance when the SNR is relatively low.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio Based on Feature Extraction and Deep Learning

Geng

Huang

Yang

et al. 2022

J. Phys.: Conf. Ser.

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In cognitive radio, spectrum sensing is used to determine whether the primary user is using the spectrum based on the signal received on a specific frequency band, thereby determining whether the secondary user can use the spectrum. The main problem faced by spectrum sensing is how to identify the existence of the primary signal under the condition of low signal-to-noise ratio (SNR). Compared with traditional technologies, deep learning methods can identify the features of input data more efficiently and accurately. Based on convolutional neural network (CNN), This paper regard spectrum sensing as a binary classification problem. In the method we proposed, different features of received are extracted, and a dataset of feature matrices obtained under different SNRs is constructed for the training of the CNN network. Experiment results show that under the condition of low signal-to-noise ratio, the performance of our method is improved compared with the traditional method, and the combination of different features can improve the sensing accuracy.

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