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首页> 外文期刊>IEEE Communications Magazine >On Leveraging Machine and Deep Learning for Throughput Prediction in Cellular Networks: Design, Performance, and Challenges
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On Leveraging Machine and Deep Learning for Throughput Prediction in Cellular Networks: Design, Performance, and Challenges

机译：关于杠杆网络吞吐量预测的利用机器和深度学习：设计，性能和挑战

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摘要

The highly dynamic wireless communication environment poses a challenge for many applications (e.g., adaptive multimedia streaming services). Providing accurate TP can significantly improve performance of these applications. The scheduling algorithms in cellular networks consider various PHY metrics, (e.g., CQI) and throughput history when assigning resources for each user. This article explains how AI can be leveraged for accurate TP in cellular networks using PHY and application layer metrics. We present key architectural components and implementation options, illustrating their advantages and limitations. We also highlight key design choices and investigate their impact on prediction accuracy using real data. We believe this is the first study that examines the impact of integrating network-level data and applying a deep learning technique (on PHY and application data) for TP in cellular systems. Using video streaming as a use case, we illustrate how accurate TP improves the end user's QoE. Furthermore, we identify open questions and research challenges in the area of AI-driven TP. Finally, we report on lessons learned and provide conclusions that we believe will be useful to network practitioners seeking to apply AI.
机译：高度动态的无线通信环境对许多应用构成挑战（例如，自适应多媒体流服务）。提供精确的TP可以显着提高这些应用的性能。当为每个用户分配资源时，蜂窝网络中的调度算法考虑各种PHY度量，（例如，CQI）和吞吐量历史。本文介绍了如何使用PHY和应用层度量来利用AI在蜂窝网络中的准确TP。我们提供了关键的架构组件和实现选项，说明了它们的优缺点。我们还使用真实数据突出显示关键设计选择并调查它们对预测准确性的影响。我们认为这是第一项研究，它检查集成网络级数据的影响并对蜂窝系统中TP应用于TP的深度学习技术（对PHY和应用程序数据）。使用视频流作为用例，我们说明了TP如何改善最终用户的QoE。此外，我们确定AI驱动TP领域的开放问题和研究挑战。最后，我们报告了学习的经验教训，并提供了我们认为对寻求申请AI的网络从业者有用的结论。

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