| Naslov: | Coati optimized hybrid neural network for efficient network slicing in 5 generation network |
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| Avtorji: | ID Sindhu, Ayya Dhurai Suceelal (Avtor)
ID Kumar, Chellappan Agees (Avtor) |
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| Datoteke: |  PDF - Predstavitvena datoteka, prenos (1,68 MB)
MD5: D829FEAD39379C26C118B8C59F3CEB17
 URL - Izvorni URL, za dostop obiščite https://ojs.midem-drustvo.si/index.php/InfMIDEM/article/view/1991
URL - Izvorni URL, za dostop obiščite \"https://www.midem-drustvo.si/journal_papers/MIDEM_55(2025)2p1.pdf
To gradivo ima še več datotek. Celoten seznam je na voljo
spodaj. |
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| Jezik: | Angleški jezik |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: |  MIDEM - Strokovno društvo za mikroelektroniko, elektronske sestavne dele in materiale
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| Povzetek: | 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. |
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| Ključne besede: | network slicing, deep learning, GhostNet, gated dilated, CNN, Coati optimization |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Datum objave: | 01.01.2025 |
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| Leto izida: | 2025 |
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| Št. strani: | str. 77-86 |
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| Številčenje: | Vol. 55, no. 2 |
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| PID: | 20.500.12556/DiRROS-30231  |
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| UDK: | 621.39:004.8 |
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| ISSN pri članku: | 0352-9045 |
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| DOI: | 10.33180/InfMIDEM2025.201 |
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| COBISS.SI-ID: | 281450499 |
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| Opomba: | Besedilo v angl.; |
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| Datum objave v DiRROS: | 18.06.2026 |
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| Število ogledov: | 146 |
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| Število prenosov: | 140 |
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| Metapodatki: |    |
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