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On eigenvalue-based cooperative spectrum sensing using feature extraction and maximum entropy fuzzy clustering

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Abstract

This article explores the scope of feature extraction and maximum entropy-based fuzzy clustering (MEFC) on eigenvalue-based cooperative spectrum sensing (CSS). The sensed primary user (PU) signal at secondary users (SUs) is pre-processed first by using principal component analysis (PCA). This pre-processed signal is sent to the fusion center (FC) for feature extraction. To extract the features, a two-stage procedure is adopted. In the first stage, the signal matrix is divided and then reassembled using ordered decomposition and recombination (ODAR) scheme. After the ODAR process, the covariance matrix and the corresponding eigenvalues are obtained. In the second stage, the features are selected based on the maximum, second maximum, and minimum eigenvalues to form a three dimensional (3D) feature vector. Feature vector obtained from measured eigenvalues are classified into channel available and unavailable class by performing clustering and classification in 3D space. The MEFC algorithm is used to train a classifier for spectrum sensing to avoid complex threshold derivation. The proposed algorithm is then compared with some of the recent decomposition-based feature extraction techniques for spectrum sensing. The results demonstrate that the proposed CSS method using the MEFC algorithm in multidimensional space provides an improvement in detection performance. This shows the effectiveness of the proposed algorithm to improve the performance of spectrum sensing.

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Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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  1. Department of Electronics and Communication, National Institute of Technology, Raipur, CG, 492010, India

    Manish Kumar Giri & Saikat Majumder

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  1. Manish Kumar Giri
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Giri, M.K., Majumder, S. On eigenvalue-based cooperative spectrum sensing using feature extraction and maximum entropy fuzzy clustering. J Ambient Intell Human Comput 14, 10053–10067 (2023). https://doi.org/10.1007/s12652-021-03670-3

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