Open Access

ARTICLE

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

by Ratih Hikmah Puspita¹, Jehad Ali^1,*, Byeong-hee Roh²

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

* Corresponding Author: Jehad Ali. Email:

Computers, Materials & Continua 2023, 75(2), 4611-4631. https://doi.org/10.32604/cmc.2023.033598

Received 21 June 2022; Accepted 15 September 2022; Issue published 31 March 2023

Abstract

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 call must be routed to a slice that meets 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 Q-learning 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.

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Keywords

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