AI-Driven Self-Organizing Networks (SON) for 5G OPEX Reduction: A Comprehensive Survey and Conceptual Framework
الشريط الجانبي للمقالة
محتوى المقالة الرئيسي
الملخص
This paper investigates the application of Artificial Intelligence (AI) in Self-Organizing Networks (SON) for 5G networks, focusing on coverage enhancement and reduction of Operational Expenditures (OPEX). A conceptual AI-Self Organizing Networks (SON) framework integrated with O RAN architecture is proposed, and an illustrative Python-based simulation is conducted to demonstrate potential trends in coverage probability, energy consumption, and estimated OPEX savings. The simulation results indicate that AI-SON can achieve near-optimal coverage (coverage probability 0.9985) while reducing energy usage and maintenance costs, with an estimated OPEX reduction of 2030% compared to baseline strategies. The study clarifies that the simulation is illustrative and not experimentally validated, providing a foundation for future rigorous evaluations.
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تفاصيل المقالة
هذا العمل مرخص بموجب Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
المراجع
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Appendices
Appendix A: Simulation Code (Summary)
The simulation used to generate the
illustrative plots is a synthetic Python model.
Key steps: 1. Define user density range (10-200
users/km^2). 2. Model coverage probability
baseline as logistic decline with density. 3.
Model AI-SON improvements by shifting
logistic parameters and adding small gains. 4.
Plot and export figures for inclusion. A
production-grade simulation would replace
synthetic models with stochastic geometry,
path-loss maps, and packet-level simulators
such as ns-3 with 5G m