Coati Optimized Hybrid Neural Network for Effi-cient Network Slicing in 5 Generation Network

Ayya Dhurai Suceelal Sindhu, Chellappan Agees Kumar

Abstract


Network slicing divides the physical network into many logical networks in order to support the variety of new applications with higher performance and flexibility needs. As a result of these applications, a massive amount of data has been generated with a huge number of mobile phones. Due to this, network slicing performance has been greatly impacted and extreme challenges have been created. To efficiently handle the challenges, this paper proposes a novel Optimal NEtwork slice CLassificatiOn Using Deep learning (ONE-CLOUD) technique, which integrates the Coati Optimization Algorithm (COA), GhostNet, and Gated Dilated Convolutional Neural Network (CNN). COA optimizes features such as user device type, packet loss ratio, and delay rate, employing GhostNet model, and Gated Dilated CNN for network slice classification. The proposed method classifies network slices into enhanced Mobile BroadBand (eMBB), Ultra-Reliable and Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). Evaluation metrics such as accuracy, precision, recall, sensitivity, specificity, throughput, and reduced latency, have been utilized to assess the efficacy of the proposed method using 5G-SliciNdd dataset. The overall accuracy of the proposed method is 5.78%, 2.78% and 4.70% higher than the existing DQN-E2E, DRL, and AAA techniques respectively.

Keywords


Network Slicing; Deep learning; GhostNet; Gated Dilated CNN; Coati Optimization.

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DOI: https://doi.org/10.33180/InfMIDEM2025.201

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