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: Identification of spectrum holes in large frequency bands is quite important for primary users and Cognitive Radio Networks (CRNs) is a suitable approach for detection of spectrum holes and for efficient spectrum utilization. Moreover, one of the important feature of CRNs is spectrum sensing which detects presence or absence of primary user so that secondary user can switch to another frequency band in time. However, swift and accurate spectrum sensing is required due to change in frequency band occupation dynamically. Thus, a statistical model is presented to perform highly efficient spectrum sensing based on the probability density functions. The main focus of research is to enhance the sensing accuracy and spectrum detection results. Filtering mechanism is utilized to remove Gaussian noise present in received primary data samples. High Quality features are obtained based on the degree of randomness and classification is performed using Support Vector Machine (SVM) classifier. Simulation results are carried out in terms of detection probability and false alarm probability using two large datasets and compared with varied classifiers such as K- Nearest Neighbour (KNN) and Logistic Regression (LR).
Keywords:
Spectrum Sensing, Sstatistical model, Support Vector Machine (SVM), Probability Density Function, Primary User (PU).
Cite Article:
"Efficient Spectrum Sensing using Robust Statistical Approach in Cognitive Radio Networks (CRNs)", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 8, page no.555 - 566, August-2022, Available :http://www.ijrti.org/papers/IJRTI2208095.pdf
Downloads:
000205328
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator