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5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algorithm

Computers, Materials & Continua 2024年第7期80卷 1179-1201页

作者： Amel Ali Alhussan S.K.Towfek Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVA 24060USA

In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic *** of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital *** essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G *** Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine ***,the model performs and yields more accurate *** work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees *** ensemble model shows better prediction performance with the coefficient of determination R squared value equal *** proposed framework is supported by several Statistical analyses,such as theWilcoxon signed-rank *** of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.

关键词： Optimization ensemble learning 5G technology artificial intelligence greylag goose optimization anova test

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