Bayesian Neural Network-Based Mobile Traffic Forecasting and Resource Management

As mobile traffic increases with the advent of 5 G an d 6G technologies, it is becoming increasingly crucial to effectivel y manage network traffic resources, which can be significantly en hanced by accurate traffic forecasting and practical management. While some machine learning and deep learning models have alr eady been utilized in the problem, the impact of uncertainty has not been paid enough attention to, and problem of corresponding resource management has not been fully considered. This study deploys the Bayesian Neural Network (BNN) to confront the joint problem of traffic forecasting and resource allocation, demonstr ating the advantages of BNN for capturing uncertainty and arran ging network resources. A BNN ensemble model further improve s forecasting performance, outperforming basic BNN and DNN models, and supporting superior resource allocation scheme com pared to even allocation scheme. Our results demonstrates that B NNs can significantly contribute to a more efficient management of network resources in modern networks.