Research and Design of a Fault Detection System for High-Band Wireless Communication Networks Using Machine Learning

This manuscript explores the design and implementation of a fault detection system for high-band wireless communication networks, utilizing machine learning technologies. As 5G and future communication technologies evolve, high-frequency communications, such as millimeter waves and terahertz waves, reveal their substantial potential in various advanced applications. However, they also encounter significant technical challenges, especially in fault detection. Addressing these challenges, this study employs Support Vector Machines (SVM) and Synthetic Minority Over-sampling Technique (SMOTE) to design a fault detection model that overcomes issues of sample imbalance and high dimensionality. The findings demonstrate that the proposed system effectively identifies and predicts network faults, enhancing the accuracy and timeliness of fault detection. This research not only offers a technological innovation but also provides a novel solution for the reliable operation and fault management of high-frequency communication networks, possessing significant academic and practical value.