Distributed average consensus optimization for cooperative spectrum sensing in cognitive radio ad hoc networks,Transactions on Emerging Telecommunications Technologies

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Distributed average consensus optimization for cooperative spectrum sensing in cognitive radio ad hoc networks
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-04-13 , DOI: 10.1002/ett.3965
Aislan Gabriel Hernandes ₁ , Taufik Abrão ₁

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In this article, we develop a parameter and optimization analyses aiming at characterizing the performance of cooperative spectrum sensing (CSS) based on the distributed average consensus (DAC), taking into account the network topology effect, represented by adjacency matrices in cognitive radio ad hoc networks. The CSS performance is analyzed by the probability of detection, probability of false alarm, and probability of miss detection, besides the decision threshold and the number of collected samples. In this work, all secondary users are equipped with a simple energy detector. Despite the DAC technique has been successfully deployed in the distributed CSS context, however, a full analytical description taking into account the joint effects of the network topology and all the optimized system parameters remains unknown. Moreover, the CSS network decision threshold was optimized using the error probability minimization criterion. Numerical results deploying extensive Monte Carlo simulations have corroborated the proposed analytical expressions.

中文翻译：

认知无线电自组织网络中协作频谱感知的分布式平均共识优化

在本文中，我们开发了一个参数和优化分析，旨在表征基于分布式平均共识（DAC）的协作频谱感知（CSS）的性能，同时考虑了以认知无线电ad hoc中的邻接矩阵为代表的网络拓扑效应网络。除了决策阈值和收集的样本数外，还通过检测概率，错误警报概率和未命中检测概率来分析CSS性能。在这项工作中，所有二级用户都配备了一个简单的能量探测器。尽管已将DAC技术成功地部署在分布式CSS上下文中，但是，考虑到网络拓扑和所有优化的系统参数的共同影响，进行完整的分析描述仍然是未知的。此外，使用错误概率最小化准则优化CSS网络决策阈值。部署广泛的蒙特卡洛模拟的数值结果证实了所提出的解析表达式。

更新日期：2020-04-13

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