Coati optimized hybrid neural network for efficient network slicing in 5 generation network - Sindhu, Ayya Dhurai Suceelal (Informacije MIDEM, 2025)
Network slicing (NS) 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, NS 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). The effectiveness of the suggested approach has been evaluated using the 5G-SliciNdd dataset, utilizing evaluation criteria like accuracy, precision, recall, sensitivity, specificity, throughput, and reduced latency. 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: CNN; Coati optimizacija; Coati optimization; deep learning; gated dilated; Gated Dilated CNN; GhostNet; globoko učenje; network slicing; rezanje omrežja