Identification of Communication Cables Based on S-Parameters and K-Nearest Neighbors Algorithm

Oumaima Bader, Dhia Haddad, Ahmed Yahia Kallel, N. Amara, O. Kanoun
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Abstract

Cable identification has a significant role in cable maintenance and fault detection. Before replacing defective cables in a network, they must be properly identified. In this paper, a novel method for coaxial communication cables identification based on scattering parameters is proposed. The input port reflection's magnitude measured by a Nano Vector Network Analyzer at 101 frequencies for 10 coaxial communication cables are used as features for the K-Nearest Neighbors algorithm. The investigation is held on cables of various lengths, dimensions and connector types. The cable's length, type and connectors are considered as a unique class. The classification accuracy reached is 99% for a test set composed of 100 measurements.
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基于s参数和k近邻算法的通信电缆识别
电缆识别在电缆维护和故障检测中具有重要的作用。在更换网络中有缺陷的电缆之前,必须对其进行正确的识别。本文提出了一种基于散射参数的同轴通信电缆识别新方法。采用纳米矢量网络分析仪在10根同轴通信电缆的101个频率下测量的输入端口反射幅度作为k -最近邻算法的特征。该调查针对各种长度、尺寸和连接器类型的电缆进行。电缆的长度、类型和连接器被认为是一个独特的类别。对于由100个测量值组成的测试集,分类准确率达到99%。
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