KT Introduces AI Monitoring System for Communication Disruptions in Remote Mountainous Areas
[Asia Economy Reporter Lim Hye-sun] KT announced on the 22nd that it has introduced 'Doctor Microwave,' an artificial intelligence (AI)-based monitoring system for microwave equipment, which is essential for providing wireless communication in remote mountainous and island areas nationwide.
Microwave equipment is a communication facility that connects sections where wired connections are difficult through wireless radio waves. According to the Korea Forest Service, about 70% of South Korea's total land area is mountainous, and statistics from the Korea Maritime Institute show that the country has as many as 3,348 islands, making microwave equipment essential for smooth communication.
KT explained, "If a microwave facility malfunctions, communication in remote mountainous and island areas is cut off," adding, "KT conducts real-time monitoring and troubleshooting to ensure that microwave equipment operates normally at all times despite climate or environmental changes."
Until now, detecting microwave equipment failures took 40 minutes, but after introducing 'Doctor Microwave,' this time has been drastically reduced to one minute, enabling more stable communication services in remote mountainous and island areas.
Doctor Microwave is characterized by its AI model, which comprehensively learns the climatic and geographical characteristics of remote mountainous and island areas, as well as alarm and performance information from network equipment, to monitor failures 24 hours a day and identify the location and cause of failures in real time.
Additionally, the technology recommends optimal recovery methods for each type of failure in real time based on existing operation and troubleshooting data. It also includes automatic learning data generation technology based on machine learning and AI model update functions, continuously improving the accuracy of failure cause analysis.
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KT plans to further advance Doctor Microwave to expand into predicting network anomalies and automating AI-based facility inspections.
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