论文标题
通过模型驱动和基于AI的技术的协同作用，移动网络中的网络编排
Network Orchestration in Mobile Networks via a Synergy of Model-driven and AI-based Techniques
论文作者
论文摘要
随着数据流量量的不断增加，靠近最终用户的战略网络位置的流行内容的缓存不仅可以增强用户体验，而且可以减轻网络中高度拥挤的链接的利用。主动缓存领域的一个主要挑战是找到在各种优化标准下托管流行内容项目的最佳位置。这些问题本质上是组合的，因此找到最佳和/或接近最佳决策在计算上是昂贵的。在本文中，提出了一个框架，旨在通过首先预测使用深卷积神经网络（CNN）与最佳位置相关的决策变量来降低基础整数数学程序的计算复杂性。 CNN通过最佳解决方案以离线方式进行了培训，然后用来解决一个小得多的优化问题，这是可用于实时决策的。数值调查表明，拟议的方法可以在线提供高质量决策；对于现实世界实现至关重要的功能。
As data traffic volume continues to increase, caching of popular content at strategic network locations closer to the end user can enhance not only user experience but ease the utilization of highly congested links in the network. A key challenge in the area of proactive caching is finding the optimal locations to host the popular content items under various optimization criteria. These problems are combinatorial in nature and therefore finding optimal and/or near optimal decisions is computationally expensive. In this paper a framework is proposed to reduce the computational complexity of the underlying integer mathematical program by first predicting decision variables related to optimal locations using a deep convolutional neural network (CNN). The CNN is trained in an offline manner with optimal solutions and is then used to feed a much smaller optimization problems which is amenable for real-time decision making. Numerical investigations reveal that the proposed approach can provide in an online manner high quality decision making; a feature which is crucially important for real-world implementations.
