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Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
IEEE Communications Surveys & Tutorials ( IF 35.6 ) Pub Date : 2019-01-01 , DOI: 10.1109/comst.2019.2924243
Yaohua Sun , Mugen Peng , Yangcheng Zhou , Yuzhe Huang , Shiwen Mao

As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, and beamformer design and computation resource management, while ML-based networking focuses on the applications in clustering, base station switching control, user association, and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use. Traditional approaches are also summarized together with their performance comparison with ML-based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies. Specifically, ML-based network slicing, infrastructure update to support ML-based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation, and so on are discussed.

中文翻译:

机器学习在无线网络中的应用:关键技术和未解决的问题

作为实现人工智能的关键技术,机器学习 (ML) 无需显式编程即可解决复杂问题。由于其在图像识别等许多实际任务中的成功应用,工业界和研究界都提倡 ML 在无线通信中的应用。本文综合考察了ML在无线通信中应用的最新进展,分为:MAC层的资源管理、网络层的组网和移动性管理、应用层的本地化。在资源管理方面的应用还包括功率控制、频谱管理、回程管理、缓存管理、波束成形设计和计算资源管理,而基于ML的网络则侧重于集群、基站切换控制、用户关联和路由等方面的应用。此外,每个方面的文献都是根据采用的 ML 技术组织的。此外,还确定了将 ML 应用于无线通信的几个条件,以帮助读者决定是否使用 ML 以及使用哪种 ML 技术。还总结了传统方法及其与基于 ML 的方法的性能比较,在此基础上阐明了调查文献采用 ML 的动机。鉴于研究领域的广泛性,提出了挑战和未解决的问题,以促进未来的研究。具体而言,基于 ML 的网络切片、支持基于 ML 范式的基础设施更新、开放数据集和研究人员平台,
更新日期:2019-01-01
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