面向电信测试的 CNN 与 K-means 聚类融合应用方案
IAA of CNN and K-means in Telecommunication Testing.
高煜涵 1张沛泓1
作者信息
- 1. 辽宁工程技术大学电子与信息工程学院，兴城 125100
- 折叠
摘要
随着5G/6G与物联网的规模化部署，电信测试面临严苛的挑战，传统信号处理方法难以满足需求。本文融合测量误差理论构建闭环智能测试系统，设计基于K-Means的网络覆盖评估模型与基于CNN的信号调制识别模型。实验表明，在5G基站测试中，系统时延误差控制在0.1ms内，功耗测量精度达±0.5%，测试效率较传统方案提升400%；研究为6G测试体系构建提供理论支撑，未来可进一步适配新场景与技术融合。Abstract
With the large-scale deployment of 5G/6G and the Internet of Things (IoT), telecommunication testing is confronted with stringent challenges, and traditional signal processing methods struggle to meet the requirements. This paper integrates the measurement error theory to construct a closed-loop intelligent testing system, and designs a K-Means-based network coverage evaluation model as well as a CNN-based signal modulation recognition model. Experiments show that in 5G base station testing, the system\'s delay error is controlled within 0.1ms, the power measurement accuracy reaches ±0.5%, and the testing efficiency is 400% higher than that of traditional solutions. This research provides theoretical support for the construction of the 6G testing system, and in the future, it can be further adapted to new scenarios and technical integration. 关键词
信息与通信工程、电信测试、K-Means聚类分析、CNN模型Key words
Information and Communication Engineering/telecommunication testing/K-Means cluster analysis/Convolutional Neural Network (CNN) model引用本文复制引用
高煜涵,张沛泓.面向电信测试的 CNN 与 K-means 聚类融合应用方案[EB/OL].(2025-12-03)[2025-12-05].http://www.paper.edu.cn/releasepaper/content/202512-4.学科分类
通信/无线通信
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