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

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

机构地区：[1]Department of AI Convergence Network,Ajou University,Suwon,16499,Korea [2]Department of Computer Engineering,and Department of AI Convergence Network,Ajou University,Suwon,16499,Korea

出　　处：《Computers, Materials & Continua》2023年第5期4611-4631,共21页计算机、材料和连续体（英文）

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).

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 dynamic.Slicing enables service providers to develop various network slice architectures.As users travel from one coverage region to another area,the callmust be routed to a slice thatmeets the same or different expectations.This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks.Rules 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 strategies.Therefore,this study discusses the network model’s design and implementation of self-optimization Fuzzy Qlearning of the decision-making algorithm for slice handover.The 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 dropped.Hence,within the network model,the call admission control(AC)method is modeled by leveraging supervised learning algorithm as prior knowledge of additional capacity.Moreover,to mitigate high complexity,the integration of fuzzy logic as well as Fuzzy Q-Learning is used to discretize state and the corresponding action spaces.The results generated from our proposal surpass the traditional methods without the use of supervised learning and fuzzy-Q learning.

关 键 词：5g network slice fuzzy q-Learning slice handover 

分 类 号：TN92[电子电信—通信与信息系统]