Optimal Generic L-Out-of-M Counting Rule for Neyman-Pearson Test in Cognitive Radio Networks

IEEE Networking Letters Pub Date : 2023-08-28 DOI:10.1109/LNET.2023.3309537

Narasimha Rao Banavathu

引用次数: 0

Abstract

This letter proposes a generic

${L}$

-out-of-

${M}$

counting rule-based sensing, wherein the cognitive radios (CRs) with non-identical receiver operating characteristic (ROC) curves and the fusion node cooperatively identify the primary user’s state. We formulate a generalized Neyman-Pearson problem to jointly optimize the individual CRs’ operational points on the ROC curves and the generic

${L}$

-out-of-

${M}$

counting rule for the CR system. Then, a fast-sensing problem is formulated to find the least number of CRs needed for practical sensing. We provide generalized solutions for any detector employed in the CR system. The proposed scheme shows superior detection performance compared to the traditional scheme.

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认知无线电网络中奈曼-皮尔逊测试的最优通用 L-Out-of-M 计数规则

本文提出了一种基于通用{L}$ -out-of-{M}$计数规则的传感方法，其中具有非相同接收器工作特性曲线（ROC）的认知无线电（CR）和融合节点可以合作识别主用户的状态。我们提出了一个广义的奈曼-皮尔逊（Neyman-Pearson）问题，以共同优化 ROC 曲线上各个 CR 的工作点和 CR 系统的通用 ${L}$ -out- ${M}$ 计数规则。然后，我们提出了一个快速传感问题，以找到实际传感所需的最少 CRs 数量。我们为 CR 系统中使用的任何探测器提供了通用解决方案。与传统方案相比，所提出的方案显示出更优越的检测性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IEEE Networking Letters

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