An AI-Driven Network Optimization Framework for the Transition from 5G to 6G

The transition from fifth-generation (5G) to sixth-generation (6G) mobile networks represents a fundamental shift in wireless communication paradigms, driven by the need for ultra-low latency, extreme data rates, native intelligence, and support for mission-critical and immersive applications. This paper presents the Rexhep Network Optimization Framework, a layered and AI-native architectural model designed to enable a smooth, efficient, and scalable evolution from 5G to 6G systems. The proposed framework integrates physical and spectrum intelligence, intelligent radio access networks (RAN) with edge computing, virtualized core networks with network slicing, and AI-driven optimization and control mechanisms. It further incorporates advanced service layers supporting extended reality (XR), digital twins, AI-based security, and mission-critical services. The framework explicitly addresses the coexistence of 5G and 6G technologies through phased deployment, hybrid optimization, and dynamic spectrum management, ensuring backward compatibility while enabling 6G-dominant capabilities. By positioning artificial intelligence as a cross-layer enabler rather than an auxiliary function, the proposed framework provides a systematic approach for network automation, resilience, and performance optimization in next-generation communication ecosystems. The presented model offers a conceptual foundation for future research, standardization, and practical deployment strategies toward 6G networks.