An Intelligent Admission Control Scheme for Dynamic Slice Handover Policy in 5G Network Slicing

作     者：Ratih Hikmah Puspita Jehad Ali Byeong-hee Roh 

作者机构：Department of AI Convergence NetworkAjou UniversitySuwon16499Korea Department of Computer Engineeringand Department of AI Convergence NetworkAjou UniversitySuwon16499Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第75卷第5期

页      面：4611-4631页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：This work was supported partially by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991514504) by theMSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2023-2018-0-01431) supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation) 

主　　题：5g network slice fuzzy q-Learning slice handover 

摘      要：5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent latency and bandwidth requirements;thus,it should be extremely flexible and *** enables service providers to develop various network slice *** users travel from one coverage region to another area,the callmust be routed to a slice thatmeets the same or different *** research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced *** of thumb which indicates the accuracy regarding the training data classification schemes within machine learning should be considered for validation and selection of the appropriate machine learning ***,this study discusses the network model’s design and implementation of self-optimization Fuzzy Qlearning of the decision-making algorithm for slice *** algorithm’s performance is assessed by means of connection-level metrics considering the Quality of Service(QoS),specifically the probability of the new call to be blocked and the probability of a handoff call being ***,within the network model,the call admission control(AC)method is modeled by leveraging supervised learning algorithm as prior knowledge of additional ***,to mitigate high complexity,the integration of fuzzy logic as well as Fuzzy Q-Learning is used to discretize state and the corresponding action *** results generated from our proposal surpass the traditional methods without the use of supervised learning and fuzzy-Q learning.