Game Theoretic Approach to QoS Oriented Machine Learning Model Development Toward 5G Network Migration Planning

With the increasing number of connected devices, the demand for mobile data has grown exponentially. The existing legacy 4G/LTE network has been unable to meet the demands and keep up with the expectations in terms of speed, latency, number of connected devices and quality of service. The situation demands transition to 5G network; however, the transition comes with significant challenges for telecom operators, requiring substantial initial investments in infrastructure, technology, and human resources. This paper presents a novel two-phased approach to facilitate efficient 5G migration using machine learning and evolutionary game theory. This study primarily focuses on the development of a simulation-based analysis and a mathematical framework. In the first phase, a machine learning model is trained on Long Term Evolution (LTE) network simulation data to predict upgradability scores for existing 4G base stations, enabling a data-driven approach based on quality of service to prioritize Radio Access Network (RAN) migration. The second phase employs evolutionary game theory to observe the migration patterns for both core and RAN components of interconnected telecom operators in three distinct scenarios over a span of five years. The simulation considers critical factors such as revenue, customer retention/acquisition, traffic volume, coverage area, 5G based service demand, and human resources requirements, providing a framework for future network migration. Our research addresses the growing strain on legacy 4G networks caused by exponential growth in mobile data traffic and connected devices. By combining machine learning and game theoretic modeling, we offer telecom operators an approach to make informed decision regarding optimal migration strategies. This study contributes to the body of knowledge on network migration strategies and provides practical insights for telecom operators navigating the complex landscape of 5G deployment and migration.