arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.00660cs.NIeess.SP
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移动网络中基于模型驱动与人工智能技术协同的网络编排

Network Orchestration in Mobile Networks via a Synergy of Model-driven and AI-based Techniques

Yantong Wang, Vasilis Friderikos

AI总结:

针对移动网络中主动缓存位置选择的组合优化难题,本文提出融合深度CNN预测与缩减规模优化的框架,实现实时高质量决策,降低计算复杂度。

摘要:

随着数据流量持续增长,在更靠近终端用户的战略网络位置缓存热门内容,不仅能提升用户体验,还能缓解网络中高度拥塞链路的利用压力。主动缓存领域的一个关键挑战是,在各种优化标准下找到承载热门内容项的最优位置。这些问题本质上是组合优化问题,因此找到最优和/或接近最优的决策在计算上代价高昂。本文提出一个框架,通过首先使用深度卷积神经网络(CNN)预测与最优位置相关的决策变量,来降低底层整数数学规划的计算复杂度。该CNN以离线方式使用最优解进行训练,随后用于生成一个规模小得多的优化问题,该问题适合实时决策。数值研究表明,所提方法能够以在线方式提供高质量的决策;这一特性对于实际部署至关重要。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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