The popularity of smart mobile devices has led to a tremendous increase in mobile traffic, which has put a considerable strain on the fifth generation of mobile communication networks (5G). Among the three application scenarios covered by 5G, ultra-high reliability and ultra-low latency (uRLLC) communication can best be realized with the assistance of artificial intelligence. For a combined 5G, edge computing and IoT-Cloud (a platform that integrates the Internet of Things and cloud) in particular, there remains many challenges to meet the uRLLC latency and reliability requirements despite a tremendous effort to develop smart data-driven methods. Therefore, this paper mainly focuses on artificial intelligence for controlling mobile-traffic flow. In our approach, we first develop a traffic-flow prediction algorithm that is based on long short-term memory (LSTM) with an attention mechanism to train mobile-traffic data in single-site mode. The algorithm is capable of effectively predicting the peak value of the traffic flow. For a multi-site case, we present an intelligent IoT-based mobile traffic prediction-and-control architecture capable of dynamically dispatching communication and computing resources. In our experiments, we demonstrate the effectiveness of the proposed scheme in reducing communication latency and its impact on lowering packet-loss ratio. Finally, we present future work and discuss some of the open issues.

中文翻译：

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基于边缘计算的 5G 网络的智能流量自适应资源分配

智能移动设备的普及导致移动流量的大幅增长，给第五代移动通信网络（5G）带来了相当大的压力。在5G覆盖的三大应用场景中，超高可靠超低时延（uRLLC）通信在人工智能的辅助下是最能实现的。特别是对于 5G、边缘计算和 IoT-Cloud（一个集成了物联网和云的平台）的组合，尽管在开发智能数据驱动方法方面付出了巨大努力，但要满足 uRLLC 延迟和可靠性要求仍然存在许多挑战。因此，本文主要关注用于控制移动流量的人工智能。在我们的方法中，我们首先开发了一种基于长短期记忆 (LSTM) 的交通流预测算法，该算法具有注意机制，可以在单站点模式下训练移动交通数据。该算法能够有效地预测交通流的峰值。对于多站点案例，我们提出了一种基于智能物联网的移动流量预测和控制架构，能够动态调度通信和计算资源。在我们的实验中，我们证明了所提出的方案在减少通信延迟方面的有效性及其对降低丢包率的影响。最后，我们介绍未来的工作并讨论一些未解决的问题。对于多站点案例，我们提出了一种基于智能物联网的移动流量预测和控制架构，能够动态调度通信和计算资源。在我们的实验中，我们证明了所提出的方案在减少通信延迟方面的有效性及其对降低丢包率的影响。最后，我们介绍未来的工作并讨论一些未解决的问题。对于多站点案例，我们提出了一种基于智能物联网的移动流量预测和控制架构，能够动态调度通信和计算资源。在我们的实验中，我们证明了所提出的方案在减少通信延迟方面的有效性及其对降低丢包率的影响。最后，我们介绍未来的工作并讨论一些未解决的问题。