Abstract

A two-level learned distributed networking (LDN) structure that uses existing machine learning (ML) algorithms and the novel Optimized Extreme Learning Machine (OELM) algorithm to perform intelligent spectrum sensing for 5G systems has been proposed and implemented. This novel technique uses input vectors like received signal strength indicator, the distance between Cognitive Radio users and gateways, and energy vectors to train the model. Extreme Learning Machine optimized by BAT algorithm outperforms the existing Machine Learning techniques in terms of detection accuracy, false alarm, detection probability and cross validation curves at different SNR scenarios.

中文翻译：

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优化的极限学习机，用于5G系统中的智能频谱感应

摘要

已经提出并实现了使用现有机器学习（ML）算法和新颖的优化极限学习机（OELM）算法为5G系统执行智能频谱感知的两级学习分布式网络（LDN）结构。这项新颖的技术使用输入矢量，例如接收信号强度指示器，认知无线电用户与网关之间的距离以及能量矢量来训练模型。通过BAT算法优化的Extreme Learning Machine在不同SNR场景下的检测准确性，误报，检测概率和交叉验证曲线方面优于现有的机器学习技术。