Artificial Intelligence-Driven Network Slicing

Utilizing the artificial intelligence (AI) supported by software-defined networking and network function virtualization has a significant impact on the performance, flexibility, and efficiency in the development of 6G network slicing. This article compares AI-driven 6G slicing networks with traditional rulesbased 5G networks, focusing on latency, data throughput, jitter, power efficiency, and bandwidth. NS-3 and MATLAB have been utilized to evaluate the networks performance. The comparison results show that AI-driven network slicing reduces average latency by 50%, boosts data throughput by 40–90%, reduces jitter by 50%, and improves power efficiency by 20–28% compared to 5G networks. These results indicate that AI-powered network slicing in 6G networks outperforms traditional methods, enabling trendier network management. This sets a standard for network segmentation research in the future deployment of 6G networks.

Authors

Publication Details

Journal
ARO-The Scientific Journal of Koya University
Published
2026-04-22
DOI
https://doi.org/10.14500/aro.12303
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence-Driven Network Slicing

Ahmad B. Al-Khalil
ARO-The Scientific Journal of Koya University
Software-Defined Networks and 5G
article

Artificial Intelligence-Driven Network Slicing

Ahmad B. Al-Khalil
article en

Abstract

Utilizing the artificial intelligence (AI) supported by software-defined networking and network function virtualization has a significant impact on the performance, flexibility, and efficiency in the development of 6G network slicing. This article compares AI-driven 6G slicing networks with traditional rulesbased 5G networks, focusing on latency, data throughput, jitter, power efficiency, and bandwidth. NS-3 and MATLAB have been utilized to evaluate the networks performance. The comparison results show that AI-driven network slicing reduces average latency by 50%, boosts data throughput by 40–90%, reduces jitter by 50%, and improves power efficiency by 20–28% compared to 5G networks. These results indicate that AI-powered network slicing in 6G networks outperforms traditional methods, enabling trendier network management. This sets a standard for network segmentation research in the future deployment of 6G networks.

ARO-The Scientific Journal of Koya UniversityVol. 14(1)
Affordable and clean energy
Openalex Percentile: Top 11%
Software-Defined Networks and 5G
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