Enhanced 6G Network Slicing Classification Using PSO-Based Feature Selection and Ensemble Learning
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In parallel with technological developments, mobile and wireless communication technologies are advancing rapidly. Today, 5G technology is widely used, while studies on 6G continue. As a result of these developments, the use of internet-connected devices is increasing both in daily life and in industrial areas. This increase in the number of devices brings critical requirements such as additional resources and energy consumption in networks, communication prioritization, and reliable data transmission. In response to these needs, 6G technology offers dynamic, flexible, and scenario-based resource allocation based on network slicing. Network slicing makes it possible to support innovative services such as super-eMBB, massive-MTC, and super-URLLC. In this paper, PSO-based feature selection and ensemble learning methods are used to solve the network slicing classification problem in 6G networks. In addition, the LIME method is applied to help better interpret model predictions and make reliable decisions.The main objective of this study is to contribute to the development of autonomous and seamless network systems that are suitable for the dynamic and complex nature of 6G. Thus, it aims to create reliable, flexible, and efficient network solutions by making the best use of the innovations offered by 6G. © 2025 IEEE.
Açıklama
IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2025 -- 3 July 2025 through 6 July 2025 -- Paris -- 222255
Anahtar Kelimeler
6G, Ensemble Learning, LIME, Network Slicing, PSO
Kaynak
International Conference on Electrical, Computer, and Energy Technologies, ICECET 2025
WoS Q Değeri
Scopus Q Değeri
N/A
