Mobile networks (MNs) are anticipated to provide unprecedented opportunities to enable a new world of connected experiences and radically shift the way people interact with everything. MNs are becoming more and more complex, driven by ever increasing complicated configuration issues and blossoming new service requirements. This complexity poses significant challenges in deployment, management, operation, optimization, and maintenance, since they require complete understanding and cognition of MNs. Artificial intelligence (AI), which deals with the simulation of intelligent behavior in computers, has demonstrated enormous success in many application domains, suggesting its potential in cognizing the state of an MN and making intelligent decisions. In this article, we first propose an AI-powered MN architecture and discuss challenges in terms of cognition complexity, decisions with high-dimensional action space, and self-adaptation to system dynamics. Then potential solutions associated with AI are discussed. Finally, we propose a deep learning approach that directly maps the state of an MN to perceived QoS, integrating cognition with the decision. Our proposed approach helps operators to make more intelligent decisions to guarantee QoS. Meanwhile, the effectiveness and advantages of our proposed approach are demonstrated on a real-world dataset involving 31,261 users over 77 stations within 5 days.

中文翻译：

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人工智能驱动的移动网络：从认知到决策


移动网络 (MN) 预计将提供前所未有的机会，打造互联体验的新世界，并从根本上改变人们与万物交互的方式。在日益复杂的配置问题和不断涌现的新服务需求的推动下，移动节点变得越来越复杂。这种复杂性给部署、管理、运营、优化和维护带来了巨大的挑战，因为它们需要对移动节点的完整理解和认知。人工智能（AI）涉及计算机中智能行为的模拟，已在许多应用领域取得了巨大成功，这表明其在认知 MN 状态和做出智能决策方面具有潜力。在本文中，我们首先提出了一种基于人工智能的 MN 架构，并讨论了认知复杂性、高维动作空间决策以及系统动态自适应方面的挑战。然后讨论与人工智能相关的潜在解决方案。最后，我们提出了一种深度学习方法，将 MN 的状态直接映射到感知的 QoS，将认知与决策相结合。我们提出的方法可以帮助运营商做出更明智的决策来保证 QoS。同时，我们提出的方法的有效性和优势在 5 天内涉及 77 个站点的 31,261 位用户的真实数据集上得到了证明。