IIARD International Institute of Academic Research and Development

RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY (RJPST )

E-ISSN 2579-0536
P-ISSN 2695-2696
VOL. 8 NO. 5 2025
DOI: 10.56201/rjpst.vol.8.no5.2025.pg43.76

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Systematic Review of Predictive Intelligence Models in Product- Centric 6G Network Planning and Optimization

Nasiru Hayatu, Abraham Ayodeji Abayomi, Abel Chukwuemeke Uzoka

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Abstract

The advent of sixth-generation (6G) mobile networks promises to revolutionize telecommunications through ultra-low latency, massive connectivity, and intelligent automation. As the industry shifts toward product-centric service delivery—focusing on user- centric applications, dynamic network slicing, and on-demand resource allocation—the role of predictive intelligence in network planning and optimization becomes increasingly vital. This systematic review synthesizes current research on predictive intelligence models applied to product-centric 6G network environments, aiming to evaluate their effectiveness, methodological diversity, and potential for real-time decision-making in highly complex systems. The review is conducted following PRISMA guidelines, encompassing studies from 2018 to 2024 across major digital libraries. Selected models include machine learning algorithms, deep learning networks, Bayesian inference systems, and hybrid approaches used for traffic forecasting, user behavior prediction, resource provisioning, and failure mitigation. The findings reveal a growing reliance on supervised and unsupervised learning, particularly in network traffic analysis and proactive quality-of-service (QoS) assurance. Furthermore, the review identifies key predictive features utilized in these models, such as historical traffic data, environmental conditions, device mobility, and service consumption patterns. Emphasis is placed on models that support intent-based networking and enable autonomous network reconfiguration. Evaluation metrics, including prediction accuracy, scalability, and computational overhead, are compared to highlight performance trade-offs in real-world deployments. The review also uncovers significant challenges, such as data sparsity, high model complexity, and the need for explainable AI to improve trust and transparency in automated decisions. Gaps in current literature point to limited research on real-

keywords:

Predictive Intelligence, 6G Network Planning, Product-Centric Networks, Machine

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