5G和B5G网络中的ML KPI预测

Nguyen Phuc Tran, Oscar Delgado, B. Jaumard, Fadi Bishay
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引用次数: 0

摘要

网络运营商在满足用户需求方面面临着新的挑战。由于高清视频流、物联网、自动驾驶等新服务的兴起以及网络流量的指数级增长,挑战出现了。在此背景下,5G和B5G网络一直在不断发展,以适应广泛的应用和用例。此外,这种演变还带来了一些新特性,比如使用网络切片创建多个端到端隔离虚拟网络的能力。然而,为了保证服务质量,运营商必须根据关键绩效指标(kpi)和分片服务水平协议(sla)对网络进行维护和优化。在本文中,我们介绍了一个机器学习(ML)模型,用于估计端到端(E2E)网络切片的5G和B5G网络的吞吐量。然后,我们将预测的吞吐量与当前网络状态相结合,得出其他网络kpi的估计,可用于进一步提高服务保证。为了评估我们的解决方案的效率,提出了一个性能指标。数值评估表明,我们的KPI预测模型在计算时间相同或几乎相同的情况下优于其他方法。
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ML KPI Prediction in 5G and B5G Networks
Network operators are facing new challenges when meeting the needs of their customers. The challenges arise due to the rise of new services, such as HD video streaming, IoT, autonomous driving, etc., and the exponential growth of network traffic. In this context, 5G and B5G networks have been evolving to accommodate a wide range of applications and use cases. Additionally, this evolution brings new features, like the ability to create multiple end-to-end isolated virtual networks using network slicing. Nevertheless, to ensure the quality of service, operators must maintain and optimize their networks in accordance with the key performance indicators (KPIs) and the slice service-level agreements (SLAs). In this paper, we introduce a machine learning (ML) model used to estimate throughput in 5G and B5G networks with end-to-end (E2E) network slices. Then, we combine the predicted throughput with the current network state to derive an estimate of other network KPIs, which can be used to further improve service assurance. To assess the efficiency of our solution, a performance metric was proposed. Numerical evaluations demonstrate that our KPI prediction model outperforms those derived from other methods with the same or nearly the same computational time.
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