基于k近邻算法的异常检测频谱感知

Lizeth Lopez-Lopez, Á. G. Andrade, G. Galaviz
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引用次数: 0

摘要

有效利用稀缺频谱是满足未来6G移动网络需求的关键。频谱感知允许二次用户在第一阶段检测未使用的频谱部分,从而实现动态频谱接入。人工智能算法已被应用于解决频谱使用(占用或空闲)的分类问题。然而,他们需要大量的培训资料。本文将频谱感知作为一个异常检测问题来研究。正常行为被定义为主(授权)用户在特定频段内不活动。因此,主要用户信号的存在是一种异常。采用k近邻算法检测频段内的异常。所得结果表明,与传统的基于能量的频谱传感技术相比,检测性能有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Anomaly Detection-based Spectrum Sensing using the k-Nearest Neighbors Algorithm
The efficient utilization of the scarce spectrum is essential to satisfy the requirements of future 6G mobile networks. Spectrum sensing allows secondary users to detect unused spectrum portions as a first stage to enable dynamic spectrum access. Artificial intelligence algorithms have been applied to solve the spectrum use (occupied or vacant) as a classification problem. However, they required vast information set for training. In this paper, spectrum sensing is addressed as an anomaly detection problem. The normal behavior is defined as the inactivity of the primary (licensed) user in a specific frequency band. Thus, an anomaly is the presence of the primary users’ signals. The k-nearest neighbors algorithm is implemented to detect the anomalies in the frequency band. The obtained results show an improvement in detection performance compared to the conventional energy-based spectrum sensing technique.
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