Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
Beijing University of Posts and Telecommunications · Princeton University · +4 more institutions
Abstract
In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage intelligent, data-driven functions enabled by the integration of machine learning (ML) notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems. For this purpose, we first present a detailed overview of a number of key types of ANNs that include recurrent, spiking, and deep neural networks, that are pertinent to wireless…
Citation impact
- FWCI
- 93.44
- Percentile
- 100%
- References
- 163
Authors
5- MCMingzhe ChenCorresponding
Beijing University of Posts and Telecommunications, Princeton University, Chinese University of Hong Kong, Shenzhen
- UCUrsula Challita
University of Edinburgh
- WSWalid Saad
Virginia Tech
- CYChangchuan Yin
Beijing University of Posts and Telecommunications
- MDMérouane Debbah
Huawei Technologies (France)
Topics & keywords
- Computer science
- Wireless network
- Wireless
- Artificial neural network
- Leverage (statistics)
- Context (archaeology)
- Artificial intelligence
- Machine learning
- Industry, innovation and infrastructure