Measuring AI-Driven Autonomy in 5G and FTTH Networks: A Benchmarking Framework for Saudi Vision 2030 Digital Infrastructure

Authors

DOI:

https://doi.org/10.63084/cognexus.v2i1.311

Keywords:

AI-Driven Autonomy, 5G Networks, Fiber-to-the-Home (FTTH), Autonomous Network Management, Performance Benchmarking, Closed-Loop Automation, Saudi Vision 2030

Abstract

Measuring autonomous network management requires evidence of authorized decisions, executed actions, and verified service outcomes, rather than improvements in throughput or prediction accuracy alone. This article proposes a technology-aware benchmarking framework for assessing operational autonomy in fifth generation (5G) and fiber-to-the-home (FTTH) networks, with Saudi Vision 2030 as the policy application context. Four normalized dimensions, verified closed-loop completion, human independence, disturbance robustness, and audit completeness, form a provisional Autonomy Assessment Score (AAS). The framework also reports network-specific service indicators and treats operational safety as a separate qualification condition. Its calculation workflow is illustrated with 4,320 synthetic management records spanning six scenarios, two network domains, and three notional management configurations. The illustrative AI-style configuration is generated through assumed probabilities, not a trained model or implemented controller; accordingly, the examples do not validate AI effectiveness or Saudi network performance. Reproducibility is supported by a fixed random seed, documented assumptions, event-level records, scoring scripts, and sensitivity analyses. Alternative score weights change numerical values, and completion overlaps substantially with robustness, underscoring the need for dimension-level reporting. The contribution is a reproducible methodological specification for comparing operational autonomy while separating autonomy, safety, and service performance. Independent simulator, testbed, and operator validation remains necessary before empirical or national-level conclusions can be drawn.

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Published

2026-05-27

How to Cite

Mustafa, M., & Mohammad Firoz. (2026). Measuring AI-Driven Autonomy in 5G and FTTH Networks: A Benchmarking Framework for Saudi Vision 2030 Digital Infrastructure. CogNexus, 2(1), 192–244. https://doi.org/10.63084/cognexus.v2i1.311

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