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Robust Spectrum Sensing Using Moving Blocks Energy Detector with Bootstrap

  • THEORY AND METHODS OF SIGNAL PROCESSING
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Abstract

This paper, energy detection using a bootstrap approach describe a proposal for spectrum sensing. The proposed model is applied in the case of small sample size and to deal with the problem of correlated test statistics to estimate a test statistic distribution. This new approach requires no prior information about the background environment and uses a bootstrap method for resampling to approximately maintain the time dependencies that distinguish the presence and absence of the signal. For the previous cases, the estimation of the distribution can be performed through two ways, parametric and nonparametric bootstrap. Firstly, we display the approximation of the distributions of the test statistic, which a fully nonparametric hypothesis testing is proposed, using bootstrap approach. Accordingly, the distribution of the test statistic can be approximated employing the empirical distribution proceeded by bootstrap**. The second way, we employ a bootstrap founded approach to approximate unknown parameters which describe the distributions of the test statistic in correlated Gaussian case, using moving blocks bootstrap to generate performing independent resampling. The proposed detector exploits the sample covariance matrix to form an estimation of a distribution of the test statistic. Through simulations, the performance of the proposed detector is analyzed and compared with conventional energy detectors (CED) on the small sample size. The obtained results reveal that our proposed method outperforms greatly even with sample of small size, which is known usually the deadlock of detection performance in conventional and classical spectrum sensing and offers a robust detection performance to enhance the detection process in case of noise uncertainty environments.

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Authors and Affiliations

  1. Faculty of Genie Electric, University of Sciences and Technology Houari Boumediene (USTHB), LISIC Laboratory, BP32, BabEzzouar, Algiers, Algeria

    A. Nasrallah & A. Hamza

  2. DSMT, Ecole Superieure des Transmissions, BP11, Kolea, Tipaza, Algeria

    A. Messani

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  1. A. Nasrallah
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  3. A. Messani
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Correspondence to A. Nasrallah.

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Nasrallah, A., Hamza, A. & Messani, A. Robust Spectrum Sensing Using Moving Blocks Energy Detector with Bootstrap. J. Commun. Technol. Electron. 67, 636–648 (2022). https://doi.org/10.1134/S1064226922080125

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