Spectrum sensing in cognitive radio networks using an ensemble of machine learning frameworks and effective feature extraction

• Published in: Journal of intelligent & fuzzy systems Vol. 44; no. 6; pp. 10495 - 10509
• Main Authors: Rose, Biji, Aruna Devi, B.
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.06.2023
• Subjects: Algorithms; Classification; Cognitive radio; Discrete Wavelet Transform; Discriminant analysis; Feature extraction; Frequencies; Gabor filters; Machine learning; Signal to noise ratio; Support vector machines; Wavelet transforms
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-230438

From the signal received on a particular frequency band, spectrum sensing (SS) is used in cognitive radio (CR) to assess whether the primary user (PU) is using the spectrum and, consequently, whether the secondary user (SU) can utilize the spectrum. The main issue with SS is determining the presence of the primary signal in a low signal-to-noise ratio (SNR). Compared to conventional technologies, machine learning techniques are more effective and accurate at identifying the qualities of input data. This paper proposes a machine learning (ML) based SS model for CR with effective feature extraction and reduction techniques. The proposed work comprises five phases: noise removal, wavelet transform, feature extraction, dimensionality reduction, and classification. Firstly, noise filtering is done on the received signal to remove the noise present in the input signal using the filters such as moving median filter (MMF), Gaussian filter (GF), and Gabor filter (GBF). After that, the filtered signal is transformed into a wavelet domain using Discrete Wavelet Transform (DWT) algorithm. Then the statistical features such as average absolute value, wavelet energy, variance, standard deviation, and peak value features are extracted from the DWT. Next, the dimensionality reduction (DR) is performed using Linear Discriminant Analysis (LDA). Finally, the classification is performed using the ensemble ML classifiers such as Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbour (KNN), which classify whether the PU signal is active or not. Simulations are carried out to analyze the efficiency of the presented models for SS. The results proved that SVM obtains the best performance for SS with higher accuracy and lower SNR.