Telecom Networks

Background: Telecommunications networks are exposed to numerous issues concerning equipment and that causes network outage, which proves very expensive. Basic maintenance methodologies like reactive or even scheduled preventive maintenance cannot cope up with the increasing trends in the facilities of telecom companies. Objective: The article examines how AI is applied to support predictive maintenance so that telecommunication networks can perform as intended with reduced downtime. Methods: The review of existing AI algorithms is presented, focusing on the ML models and deep learning methods. Network operations and maintenance logs are analyzed for data to assess the capabilities of the AI models in terms of prediction. It identifies and analyses such quantifiable parameters as the failure rate prediction accuracy and the response time cut. Results: Computerisation of the forecast maintenance revealed a corresponding decrease in equipment failure incidences and generally reduced time lost due to unscheduled stops. Through the improved network performance, the response to potential threats was quicker than before and services became more reliable and inexpensive to offer. Conclusion: To reduce network outages, reduce network vulnerability, and maximize the efficiency of telecommunications operations, the use of AI-based predictive maintenance can be viewed as a prospect. As technology advances, newer versions of AI algorithms will provide improved predictive strength and incorporation into the telecommunications system.