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.

中文翻译：

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在蜂窝网络中利用机器和深度学习进行吞吐量预测：设计、性能和挑战

高度动态的无线通信环境对许多应用程序（例如，自适应多媒体流服务）提出了挑战。提供准确的 TP 可以显着提高这些应用程序的性能。在为每个用户分配资源时，蜂窝网络中的调度算法会考虑各种 PHY 指标（例如，CQI）和吞吐量历史。本文解释了如何使用 PHY 和应用层指标在蜂窝网络中利用 AI 进行准确的 TP。我们介绍了关键的架构组件和实现选项，说明了它们的优点和局限性。我们还强调了关键设计选择，并使用真实数据研究了它们对预测准确性的影响。我们相信这是第一项研究整合网络级数据和应用深度学习技术（在 PHY 和应用程序数据上）对蜂窝系统中的 TP 的影响。使用视频流作为用例，我们说明了准确的 TP 如何提高最终用户的 QoE。此外，我们确定了人工智能驱动的 TP 领域的开放性问题和研究挑战。最后，我们报告吸取的经验教训并提供我们认为对寻求应用人工智能的网络从业者有用的结论。