Artificial Intelligence-Driven Network Slicing
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.
Fields of Study & Subjects
Software-Defined Networks and 5G
Telecommunications and Broadcasting Technologies
IoT and Edge/Fog Computing
Publication details
- Journal / source
- ARO-The Scientific Journal of Koya University
- Publisher
- Koya University
- Volume
- 14
- Issue
- 1
- Pages
- 212–221
- Publication date
- 22 April 2026
- Language
- EN