| Title: | Coati optimized hybrid neural network for efficient network slicing in 5 generation network |
|---|
| Authors: | ID Sindhu, Ayya Dhurai Suceelal (Author)
ID Kumar, Chellappan Agees (Author) |
|---|
| Files: |  PDF - Presentation file, download (1,68 MB)
MD5: D829FEAD39379C26C118B8C59F3CEB17
 URL - Source URL, visit https://ojs.midem-drustvo.si/index.php/InfMIDEM/article/view/1991
URL - Source URL, visit https://ojs.midem-drustvo.si/index.php/InfMIDEM/article/view/1991
This document has even more files. Complete list of files is available
below. |
|---|
| Language: | English |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: |  MIDEM - Society for Microelectronics, Electronic Components and Materials
|
|---|
| Abstract: | 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: | network slicing, deep learning, GhostNet, gated dilated, CNN, Coati optimization |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Publication date: | 01.01.2025 |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | str. 77-86 |
|---|
| Numbering: | Vol. 55, no. 2 |
|---|
| PID: | 20.500.12556/DiRROS-30231  |
|---|
| UDC: | 621.39:004.8 |
|---|
| ISSN on article: | 0352-9045 |
|---|
| DOI: | 10.33180/InfMIDEM2025.201 |
|---|
| COBISS.SI-ID: | 281450499 |
|---|
| Note: | Besedilo v angl.; |
|---|
| Publication date in DiRROS: | 18.06.2026 |
|---|
| Views: | 149 |
|---|
| Downloads: | 141 |
|---|
| Metadata: |    |
|---|
|
:
|
Copy citation |
|---|
| | | | Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
