20.500.12556/DiRROS-30231 Coati optimized hybrid neural network for efficient network slicing in 5 generation network Coatijevo optimizirano hibridno nevronsko omrežje za učinkovito rezanje omrežja v omrežju petih generacij 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. Razrez omrežja (NS) razdeli fizično omrežje na več logičnih omrežij, da bi podprl različne nove aplikacije z večjo zmogljivostjo in prilagodljivostjo. Zaradi teh aplikacij se je z velikim številom mobilnih telefonov ustvarila ogromna količina podatkov. To je močno vplivalo na zmogljivost omrežja NS in povzročilo izjemne izzive. Za učinkovito obvladovanje teh izzivov članek predlaga novo tehniko optimalne klasifikacije omrežnih rezin z uporabo globokega učenja (ONE-CLOUD), ki združuje algoritem COA (Coati Optimization Algorithm), GhostNet in gated dilated konvolucijsko nevronsko mrežo (CNN). COA optimizira lastnosti, kot so vrsta uporabniške naprave, stopnja izgube paketov in stopnja zamude, pri čemer uporablja model GhostNet in Gated Dilated CNN za klasifikacijo omrežnih rezin. Predlagana metoda razvršča omrežne rezine v izboljšano mobilno širokopasovno omrežje (eMBB), izjemno zanesljive komunikacije z nizko zakasnitvijo (URLLC) in množične komunikacije strojnega tipa (mMTC). Učinkovitost predlaganega pristopa je bila ocenjena z uporabo podatkovne zbirke 5G-SliciNdd, pri čemer so bila uporabljena merila za ocenjevanje, kot so natančnost, točnost, priklic, občutljivost, specifičnost, prepustnost in zmanjšana zakasnitev. Skupna natančnost predlagane metode je za 5,78 %, 2,78 % in 4,70 % višja od obstoječih tehnik DQN-E2E, DRL in AAA. network slicing deep learning GhostNet gated dilated CNN Coati optimization rezanje omrežja globoko učenje GhostNet Gated Dilated CNN Coati optimizacija true false true Angleški jezik Slovenski jezik Neznano 2026-06-17 19:10:42 2026-06-18 06:02:35 2026-07-02 03:59:34 0000-00-00 00:00:00 2025 0 0 Besedilo v angl.; str. 77-86 no. 2 Vol. 55 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2025-01-01 621.39:004.8 0352-9045 10.33180/InfMIDEM2025.201 281450499 MIDEM_55(2025)2p1.pdf MIDEM_55(2025)2p1.pdf 1 D829FEAD39379C26C118B8C59F3CEB17 9ab067586fea45f0684f3b990fad79fb10c19c051879a940fc1ab8a07e759128 734f26e6-7528-11f1-b0d9-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=46807 https://ojs.midem-drustvo.si/index.php/InfMIDEM/article/view/1991 1 799fb46d-6a6f-11f1-97b2-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=45801 \"https://www.midem-drustvo.si/journal_papers/MIDEM_55(2025)2p1.pdf 1 69e0014f-7528-11f1-b0d9-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=46784 https://ojs.midem-drustvo.si/index.php/InfMIDEM/article/view/1991 1 8ac30b1b-6aca-11f1-a3b9-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=45843 RAZ_Sindhu_Ayya_Dhurai_Suceelal_2025.pdf RAZ_Sindhu_Ayya_Dhurai_Suceelal_2025.pdf 1 F8FC4DEB497B2B9E36D970DA89DABA64 943435c2b0e8847462d515aa27b8ae887b4cd32c89a695a951e78ab1105726da 75bf65fa-7384-11f1-a785-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=46399 Strokovno društvo za mikroelektroniko, elektronske sestavne dele in materiale Informacije MIDEM 0 0 0