Abstract
Compressive spectrum sensing is an advanced technique to improve the detection process in cognitive radio networks. It aims at reconstructing sparse signals from only few measurements and then achieving spectrum sensing process using only the estimated signal. In cooperative cognitive radio networks, security, time, and uncertainty issues can affect the detection performance when external malicious users try to join the network. Thus, enhanced compressive spectrum sensing techniques are extremely needed. We proposed a novel combination based on the efficiency of the Bayesian recovery inference and the strengths of the Chaotic sensing matrices to accelerate the spectrum sensing, deal with uncertainty in measurements, and provide inherent security of the system. The proposed technique is implemented and tested using several performance metrics. The simulation results prove that our proposed technique is highly efficient over the acquisition and the recovery processes.
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Benazzouza, S., Ridouani, M., Salahdine, F., Hayar, A. (2021). A Secure Bayesian Compressive Spectrum Sensing Technique Based Chaotic Matrix for Cognitive Radio Networks. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_63
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DOI: https://doi.org/10.1007/978-3-030-73689-7_63
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