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arXiv 2003.00866cs.NIeess.SP
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在未来无线网络中启用AI:数据生存周期观点

Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective

Dinh C. Nguyen, Peng Cheng, Ming Ding, David Lopez-Perez, Pubudu N. Pathirana, Jun Li, Aruna Seneviratne, Yonghui Li, H. Vincent Poor

摘要:

近年来,移动计算和物联网(IoT)网络的快速部署,这主要归因于无线系统的通信和感测能力的增加。大数据分析、普适计算以及最终的人工智能(AI)都将被部署在物联网之上,并创造一个以数据驱动AI为特征的新世界。在这种背景下,一种融合人工智能和无线通信的新模式,称为无线人工智能,将人工智能前沿推向网络边缘,被广泛认为是未来智能网络演进的关键推动因素。为此,我们从数据驱动的角度对无线AI的最新研究进行了全面的调查。具体来说,我们首先提出了一种新的无线AI架构,它涵盖了无线网络中五个关键的数据驱动AI主题,包括感知AI,网络设备AI,访问AI,用户设备AI和数据起源AI。然后,对于每个数据驱动的AI主题,我们概述了如何使用AI方法来解决新兴的数据相关问题,并展示了AI如何增强无线网络功能。特别是,与其他相关的调查论文相比,我们对无线AI在各种数据驱动领域的应用进行了深入的讨论,其中AI被证明对无线网络设计和优化非常有用。最后,研究的挑战和未来的愿景也进行了讨论,以促进在这一有前途的领域进一步研究。

英文摘要:

Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area.

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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