Mobile networks (MN) are anticipated to provide unprecedented opportunities to enable a new world of connected experiences and radically shift the way people interact with everything. MN are becoming more and more complex, driven by ever-increasingly complicated configuration issues and blossoming new service requirements. This complexity poses significant challenges in deployment, management, operation, optimization, and maintenance, since they require a complete understanding and cognition of MN. 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 MN and making intelligent decisions. In this paper, we first propose an AI-powered mobile network architecture and discuss challenges in terms of cognition complexity, decisions with high-dimensional action space, and self-adaption to system dynamics. Then, potential solutions that are associated with AI are discussed. Finally, we propose a deep learning approach that directly maps the state of MN to perceived QoS, integrating cognition with the decision. Our proposed approach helps operators in making more intelligent decisions to guarantee QoS. Meanwhile, the effectiveness and advantages of our proposed approach are demonstrated on a real-world dataset, involving $31261$ users over $77$ stations within $5$ days.

翻译：预计移动网络(MN)将提供前所未有的机会,使新的世界能够产生相互联系的经验,并从根本上改变人们与一切互动的方式。在日益复杂的配置问题和新的服务要求的推动下,MN正在变得越来越复杂。这种复杂性在部署、管理、操作、优化和维护方面构成重大挑战,因为它们需要完全理解和认识MN。人工智能(AI)处理计算机智能行为模拟,在许多应用领域都表现出巨大的成功,表明它有可能使MN状态与明智决策相适应。在本文件中,我们首先提议一个AI动力移动网络结构,讨论认知复杂性、具有高度行动空间的决定以及对系统动态的自我调整方面的挑战。然后,讨论与MNM(AI)有关的潜在解决办法。最后,我们提议了一个深度学习方法,直接绘制MN状态与感知QS状态的地图,将认知与决定结合起来。我们提议的方法有助于操作者作出更明智的决定,保证QS$。同时,我们提出的数字超过5美元的数据站的有效性和优势在5美元上展示。