Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks

Perveen, Abida and Abozariba, Raouf and Patwary, Mohammad and Aneiba, Adel (2021) Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks. Neural Computing and Applications. ISSN 0941-0643

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Abstract

Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN.

Item Type: Article
Identification Number: https://doi.org/10.1007/s00521-021-06206-0
Date: 18 June 2021
Uncontrolled Keywords: Open radio access network O-RAN, traffic forecasting, fuzzy-logic, admission control, resource allocation
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Raouf Abozariba
Date Deposited: 22 Jul 2021 14:33
Last Modified: 22 Jul 2021 14:33
URI: http://www.open-access.bcu.ac.uk/id/eprint/11983

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