TY - BOOK
T1 - White paper on machine learning in 6G wireless communication networks
AU - 6G White Paper
A2 - Ali, Samad
A2 - Saad, Walid
A2 - Rajatheva, Nandana
A2 - Chang, Kapseok
A2 - Steinbach, Daniel
A2 - Sliwa, Benjamin
A2 - Wietfeld, Christian
A2 - Mei, Kai
A2 - Shiri, Hamid
A2 - Zepernick, Hans-Jürgen
A2 - Chu, Thi My Chinh
A2 - Ahmad, Ijaz
A2 - Huusko, Jyrki
A2 - Suutala, Jaakko
A2 - Bhadauria, Shubhangi
A2 - Bhatia, Vimal
A2 - Mitra, Rangeet
A2 - Amuru, Saidhiraj
A2 - Abbas, Robert
A2 - Shao, Baohua
A2 - Capobianco, Michele
A2 - Yu, Guanghui
A2 - Claes, Maelick
A2 - Karvonen, Teemu
A2 - Chen, Mingzhe
A2 - Girnyk, Maksym
A2 - Malik, Hassan
N1 - Submitted manuscript titled as "6G white paper on machine learning in wireless communication networks".
Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
UR - https://www.6gchannel.com/portfolio-posts/6g-white-paper-machine-learning/
M3 - Other report
T3 - 6G Research Visions
BT - White paper on machine learning in 6G wireless communication networks
PB - University of Oulu
CY - Finland
ER -