Base Station Transceiver Network Monitoring and Prediction Model Using Deep Neural Network for Enhanced Network Performance and Quality of Service

This paper presents an innovative deep neural network (DNN)-based approach for the real-time surveillance and failure prognostication of Base Transceiver Station (BTS) networks.The model utilizes historical network performance metrics such as call setup success rate, handover success rate, cell availability, percentage traffic channel, call drop rate, and power availability to forecast BTS network failures with an impressive accuracy prediction of 99.8%.In contrast to conventional methods that are often labor-intensive, this paradigm delivers scalable and efficient predictions, thereby facilitating proactive management of the network.The findings underscore the model's capability to enhance the quality of service by providing timely insights into the operational health of the network.By mitigating the shortcomings inherent in traditional systems, the proposed DNN model represents a significant advancement in telecommunications monitoring and management.