A Unified Data and Analytics Architecture for Telecom Network Evolution and Generational Upgrades
Keywords:
Unified Data Architecture, Telecom Network Evolution, 5G/6G Networks, Artificial Intelligence, Graph Neural Networks, Deep LearningAbstract
The rapid evolution of telecom networks toward 5G and beyond necessitates a unified, intelligent framework capable of handling large-scale heterogeneous data and enabling real-time decision-making. This study proposes a Unified Data and Analytics Architecture (UDAA) that integrates multi-source telecom data into a scalable data fabric using cloud-edge computing and software-defined networking principles. The framework employs graph-based data modeling and advanced deep learning mechanisms, including Graph Neural Networks and reinforcement learning, to perform predictive analytics, anomaly detection, and resource optimization. A closed-loop automation strategy is incorporated through SDN/NFV orchestration to enable autonomous network control and dynamic scalability. Experimental results demonstrate significant improvements in latency reduction, prediction accuracy (~91%), and overall network efficiency compared to existing methods. The proposed architecture ensures interoperability, scalability, and security, providing a robust foundation for seamless generational upgrades toward 6G and AI-native telecom networks.
DOI: https://doi.org/10.17762/ijisae.v12i21s.8211
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