Artificial Intelligence-Driven Network Slicing
A Comparative Study of 5G and 6G Automation Capabilities
DOI:
https://doi.org/10.14500/aro.12303Keywords:
5G, 6G, Network function virtualization, Network slicing, Software-defined networkingAbstract
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.
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Copyright (c) 2026 Ahmad B. Al-Khalil
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Accepted 2026-01-08
Published 2026-04-22
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