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- W3030078830 abstract "Abstract The Gini index detector (GID) was recently proposed for cooperative spectrum sensing (CSS) in cognitive radio networks. It has low computational complexity, robustness against unequal and time-varying noise and received signal powers, and can outperform state-of-the-art detectors. In this article, artificial neural networks (ANNs) are applied to map the CSS system variables into those that parameterize the probability distributions of the GID test statistic under the hypotheses of absence ( H 0 ) and presence ( H 1 ) of the primary sensed signal. The results concerning the goodness-of-fit of the GID test statistic to candidate probability distributions demonstrate that the Stable distribution adequately characterizes the statistic under H 0 , whereas the Generalized Extreme Value distribution best applies to H 1 . Two ANNs are developed to establish the system-to-distribution parameter mapping, allowing theoretical calculations of the CSS performance metrics and the decision threshold via closed-form expressions. The theoretical results are verified by computer simulations." @default.
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- W3030078830 date "2020-07-01" @default.
- W3030078830 modified "2023-09-26" @default.
- W3030078830 title "System-to-distribution parameter mapping for the Gini index detector test statistic via artificial neural networks" @default.
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- W3030078830 doi "https://doi.org/10.1016/j.compeleceng.2020.106692" @default.
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