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Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

机译：机器学习在无线网络中的应用：关键技术和未解决的问题

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摘要

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.
机译：作为启用人工智能的关键技术，机器学习（ML）能够解决复杂的问题而无需进行显式编程。由于其在许多实际任务（例如图像识别）中的成功应用，业界和研究界都提倡ML在无线通信中的应用。本文对ML在无线通信中的最新应用进行了全面的综述，其分类为：MAC层中的资源管理，网络层中的网络和移动性管理以及应用层中的本地化。资源管理中的应用进一步包括功率控制，频谱管理，回程管理，缓存管理以及波束形成器设计和计算资源管理，而基于ML的联网则侧重于集群，基站切换控制，用户关联和路由中的应用。此外，各个方面的文献都是根据所采用的机器学习技术进行整理的。另外，确定了将ML应用于无线通信的几种条件，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，以及它们与基于ML的方法的性能比较，在此基础上，阐明了被调查文献采用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。具体来说，讨论了基于ML的网络切片，支持基于ML的范式的基础结构更新，面向研究人员的开放数据集和平台，针对ML实施的理论指导等。

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