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Time and Space Complexity Reduction of KFDA-based LTE Modulation Classification

International Journal of Sensors, Wireless Communications and Control 2023年第2期13卷 117-129页

作者： Kadoun, Iyad Bizaki, Hossein Khaleghi Department of Electrical and Electronic Engineering Malek Ashtar University of Technology Tehran Iran

Background: Kernel Fisher discriminant analysis (KFDA) is a nonlinear discrimination technique for improving automatic modulation classification (AMC) accuracy. Our study showed that the higher-order cumulants (HOCs) of the Long-term evolution (LTE) modulation types are nonline-arly separable, so the KFDA technique is a good solution for its modulation classification problem. Still, research papers showed that the KFDA suffers from high time and space computational com-plexity. Some studies concentrated on reducing the KFDA time complexity while preserving the AMC performance accuracy by finding faster calculation techniques, but unfortunately, they couldn't reduce the space complexity. Objective: This study aims to reduce the time and space computational complexity of the KFDA algorithm while preserving the AMC performance accuracy. Methods: Two new time and space complexity reduction algorithms have been proposed. The first algorithm is the most discriminative dataset points (mddp) algorithm, while the second is the k-nearest neighbors-based clustering (KNN-C) algorithm. Results: The simulation results show that these algorithms could reduce the time and space complexi-ties, but their complexity reduction is a function of signal-to-noise ratio (SNR) values. On the other hand, the KNN-C-based KFDA algorithm has less complexity than the mddp-based KFDA algo-rithm. Conclusion: The time and space computation complexity of the KFDA could be effectively reduced using mddp and KNN-C algorithms;as a result, its calculation became much faster and had less storage size. © 2023 Bentham Science Publishers.

关键词： Automatic modulation classification higher-order cumulants (HOCs) kernel fisher discriminant analysis long-term evolution mddp algorithm signal-to-noise ratio

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