MACHINE LEARNING FOR TRAFFIC PREDICTION AND COMMUNICATION EFFICIENT DATA ANALYTIC IN WIRELESS NETWORKS

Abstract

With the exponential growth of available data, deep learning has emerged as a fundamental tool for interpreting data abstractions and constructing computational models. It has revolutionized our understanding of information processing, facilitating exploration across diverse domains such as text and signal analysis, image and audio recognition, social network analysis, and bioinformatics. The overarching goal of this research is to minimize wireless network traffic and operational costs for mobile users and network operators, respectively. The integrated framework endeavors to develop predictive models by analyzing the behavior of mobile users within wireless networks and designing efficient, task-oriented models in wireless networks. Particularly, our research taps into machine learning to learn and forecast mobile user behaviors, design semantic communication systems over noisy channels, and implement unsupervised distributed functional compression over wireless channels.