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首页> 外文期刊>IEEE transactions on industrial informatics >Data-Augmentation-Based Cellular Traffic Prediction in Edge-Computing-Enabled Smart City
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Data-Augmentation-Based Cellular Traffic Prediction in Edge-Computing-Enabled Smart City

机译：基于数据增强的蜂窝流量预测，在Edge Computing的智能城市中

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摘要

With the massive deployment of 5G cellular infrastructures, traffic prediction has become an indispensable part of the cellular resource management system in order to provide reliable and fast communication services that can meet the increasing quality-of-service requirements of smart city. A promising approach for handling this problem is to introduce intelligent methods to implement a highly effective and efficient cellular traffic prediction model. Meanwhile, integrating the multiaccess edge computing framework in 5G cellular networks facilitates the application of intelligent traffic prediction models by enabling their implementation at the network edge. However, the data shortage and privacy issues may still be obstacles for training a robust and accurate prediction model at the edge. To address these issues, we propose a data-augmentation-based cellular traffic prediction model (ctGAN-S2S), where an effective data augmentation submodel based on generative adversarial networks is proposed to improve the prediction performance while protecting data privacy, and a long-short-term-memory-based sequence-to-sequence submodel is used to achieve the flexible multistep cellular traffic prediction. The experimental results on a real-world city-scale cellular traffic dataset reveal that our ctGAN-S2S model achieves up to 48.49% improvement of the prediction accuracy compared to four typical reference models.
机译：随着5G蜂窝基础设施的大规模部署，交通预测已成为蜂窝资源管理系统的不可或缺的一部分，以提供可靠和快速的通信服务，可以满足智能城市的越来越多的服务质量要求。处理此问题的有希望的方法是引入实现高效高效的蜂窝交通预测模型的智能方法。同时，在5G蜂窝网络中集成了MultiAccess Edge计算框架通过在网络边缘实现其实现，促进智能流量预测模型的应用。然而，数据短缺和隐私问题可能仍然是培训在边缘处的稳健和准确的预测模型的障碍。为了解决这些问题，我们提出了一种基于数据增强的蜂窝流量预测模型（CTGAN-S2S），其中提出了基于生成的对抗网络的有效数据增强子模型，以改善保护数据隐私的预测性能，以及长期基于短期内存的序列到序列子模型用于实现灵活的多步蜂窝流量预测。实验结果对现实世界城市规模的蜂窝交通数据集显示，与四种典型参考模型相比，我们的CTGAN-S2S模型可实现预测准确性的提高高达48.49％。

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