White paper on machine learning in 6G wireless communication networks
|Author:||Ali, Samad1; Saad, Walid2; Steinbach, Daniel3 (eds.)|
1University of Oulu, 6G Flagship
|Online Access:||PDF Full Text (PDF, 24.5 MB)|
|Persistent link:|| http://urn.fi/urn:isbn:9789526226736
Oulu : University of Oulu,
|Publish Date:|| 2020-06-30
This white paper discusses various topics, advances, and projections regarding machine learning (ML) in wireless communications. Sixth generation (6G) wireless communications networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research have enabled a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is made possible by the availability of advanced ML models, large datasets, and high computational power. In addition, the ever-increasing demand for connectivity will require even more extensive innovation in 6G wireless networks. Consequently, ML tools will play a major role in solving the new problems in the wireless domain. In this paper, we offer a vision of how ML will impact wireless communications systems. We first provide an overview of the ML methods that have the highest potential to be used in wireless networks. We then discuss the problems that can be solved by using ML in various layers of the network such as the physical, medium-access, and application layers. Zero-touch optimization of wireless networks using ML is another interesting aspect discussed in this paper. Finally, at the end of each section, a set of important future research questions is presented.
6G research visions
|Type of Publication:||
D4 Published development or research report or study
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
© University of Oulu, 2020. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.