Chen Min, Miao Yiming, Gharavi Hamid, Hu Long, Humar Iztok
IEEE Trans Cogn Commun Netw. 2019;6(2). doi: 10.1109/tccn.2019.2953061.
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.
智能移动设备的普及导致移动流量大幅增长，这给第五代移动通信网络（5G）带来了相当大的压力。在5G涵盖的三种应用场景中，超高可靠性和超低延迟（uRLLC）通信在人工智能的辅助下能够得到最佳实现。特别是对于5G、边缘计算和物联网云（一个集成物联网和云的平台）的组合而言，尽管人们付出了巨大努力来开发智能数据驱动方法，但要满足uRLLC延迟和可靠性要求仍面临许多挑战。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，我们首先开发一种基于带有注意力机制的长短期记忆（LSTM）的流量预测算法，以单站点模式训练移动流量数据。该算法能够有效地预测流量峰值。对于多站点情况，我们提出一种基于智能物联网的移动流量预测与控制架构，能够动态分配通信和计算资源。在我们的实验中，我们证明了所提方案在降低通信延迟及其对降低丢包率的影响方面的有效性。最后，我们介绍了未来的工作并讨论了一些开放问题。
