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Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks
Neural Computing and Applications ( IF 4.5 ) Pub Date : 2021-06-18 , DOI: 10.1007/s00521-021-06206-0
Abida Perveen , Raouf Abozariba , Mohammad Patwary , Adel Aneiba

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.



中文翻译:

联合 5G-open RAN 网络中的动态流量预测和基于模糊的优化准入控制

为高密度流量需求提供连接是未来无线网络的主要承诺之一。开放式无线电接入网络 (O-RAN) 是确保异构网络中此类连接的关键驱动因素之一。尽管研究人员对该领域产生了浓厚的兴趣,但确保有效的网络资源分配和利用仍然存在关键挑战。本文提出了一种动态流量预测方案来预测联合 O-RAN 中的未来流量需求。利用有关用户需求和网络容量的信息,我们通过模糊逻辑优化提出了一个完全可重构的准入控制框架。我们还对来自各种论文的基准测试的几个参数(用户满意度、利用率增益和公平性)进行了详细分析。结果表明,所提出的基于预测和模糊逻辑的准入控制框架显着增强了公平性,并在不牺牲资源利用率的情况下提供有保证的体验质量。此外,我们已经证明所提出的框架可以容纳在联合 O-RAN 中同时连接的大量设备。

更新日期:2021-06-18
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