Опубликована: Окт. 10, 2023
The Network Slice Selection Function (NSSF) in heterogeneous technology environments is a complex problem, which still does not have fully acceptable solution.Thus, the implementation of new network selection strategies represents an important issue development, mainly due to growing demand for applications and scenarios involving 5G future networks.This work then presents integrated solution NSSF called Decision-Aid Framework (NSSF DAF), consists distributed part executed on user's equipment (e.g.smartphones, Unmanned Aerial Vehicles, IoT brokers), functioning as transparent service, another at Edge operator or service provider.It requires low consumption computing resources from mobile devices offers complete independence operator.For this purpose, protocols software tools are used classify slices.This employs fourteen multicriteria methods aid decision-making: ARAS, COCOSO, CODAS, COPRAS, EDAS, MABAC, MAIRCA, MARCOS, MOORA, OCRA, PROMETHEE II, SPOTIS, TOPSIS VIKOR.The general objective verify similarity among these slice classification process, considering specific scenario, towards framework.It also uses machine learning through K-means clustering algorithm, adopting hybrid implement operate multi-domain slicing networks.Testbeds were conducted validate proposed framework, mapping adequate quality requirements.The results indicate real possibility offering problem that can be implemented Edge, Core, even Radio Base Station itself, without incremental computational cost end equipment, allowing experience.
Язык: Английский