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

TL;DR

This survey reviews recent machine learning techniques applied to wireless networks, covering resource management, networking, and localization, highlighting challenges, open issues, and future research directions.

Contribution

It provides a comprehensive classification of ML applications in wireless communication and offers insights into conditions, traditional approaches, and challenges for future work.

Findings

ML improves resource management efficiency

ML-based networking enhances clustering and routing

Open issues include network slicing and data sharing platforms

Abstract

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, 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, and 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, where 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.