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.

中文翻译：

---

基于人工神经网络的基尼指数检测器检验统计量的系统到分布参数映射

摘要 基尼指数检测器（GID）最近被提出用于认知无线电网络中的协作频谱感知（CSS）。它具有低计算复杂度、对不等和时变噪声和接收信号功率的鲁棒性，并且可以胜过最先进的检测器。在本文中，人工神经网络 (ANN) 被应用于将 CSS 系统变量映射到那些在主要感测信号不存在 (H 0 ) 和存在 ( H 1 ) 的假设下参数化 GID 测试统计量的概率分布的变量. 关于 GID 检验统计量与候选概率分布的拟合优度的结果表明，稳定分布充分表征了 H 0 下的统计量，而广义极值分布最适用于 H 1 。开发了两个 ANN 来建立系统到分布的参数映射，允许通过闭式表达式对 CSS 性能指标和决策阈值进行理论计算。通过计算机模拟验证了理论结果。