Realtime mobile bandwidth and handoff predictions in 4G/5G networks
Realtime mobile bandwidth and handoff predictions in 4G/5G networks
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DOI：
10.1016/j.comnet.2021.108736
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发表时间：
2022-01-13
影响因子：
5.6
通讯作者：
Liu, Yong
中科院分区：
文献类型：
--
作者：
Mei, Lifan;Gou, Jinrui;Liu, Yong
移动应用程序越来越依赖高吞吐量和低延迟的内容交付，而无线接入链路上的可用带宽本身就是时变的。由于用户移动性导致的基站和接入模式之间的切换给提供高水平的用户体验(QOE)带来了额外的挑战。预测可用带宽和即将到来的切换的能力将为应用程序提供宝贵的回旋余地，以进行主动调整，以避免显著的QOE下降。在本文中，我们探讨了在4G/LTE和5G网络中实时移动带宽和切换预测的可能性和准确性。为了实现这一目标，我们从公共交通系统收集具有丰富带宽、频道和上下文信息的长时间连续轨迹。我们开发了递归神经网络模型来挖掘固定路由移动场景中带宽演化的时间模式。我们的模型始终优于传统的单变量和多变量带宽预测模型。对于下一秒带宽预测，在平均绝对误差(MAE)方面，我们的模型在4G轨迹上平均比其他方法好15.28%，在5G轨迹上比其他方法好15.37%。针对4G和5G共存的网络，我们提出了一个新的4G和5G切换预测问题，这对于在现实的5G场景中实现良好的应用性能具有重要意义。我们开发了基于分类和回归的预测模型，在最近的5G数据集中预测4G和5G之间的切换时，准确率达到了80%以上。
Mobile apps are increasingly relying on high-throughput and low-latency content delivery, while the available bandwidth on wireless access links is inherently time-varying. The handoffs between base stations and access modes due to user mobility present additional challenges to deliver a high level of user Quality-of-Experience (QoE). The ability to predict the available bandwidth and the upcoming handoffs will give applications valuable leeway to make proactive adjustments to avoid significant QoE degradation. In this paper, we explore the possibility and accuracy of realtime mobile bandwidth and handoff predictions in 4G/LTE and 5G networks. Towards this goal, we collect long consecutive traces with rich bandwidth, channel, and context information from public transportation systems. We develop Recurrent Neural Network models to mine the temporal patterns of bandwidth evolution in fixed-route mobility scenarios. Our models consistently outperform the conventional univariate and multivariate bandwidth prediction models. For the next second bandwidth prediction, in terms of Mean Absolute Error (MAE), our model is on average 15.28% better than the other methods in 4G traces and 15.37% better than the other methods in 5G traces. For 4G & 5G co-existing networks, we propose a new problem of handoff prediction between 4G and 5G, which is important to achieve good application performance in realistic 5G scenarios. We develop classification and regression based prediction models, which achieve more than 80% accuracy in predicting handoffs between 4G and 5G in a recent 5G dataset.