Thirty years of machine learning: The road to Pareto-optimal wireless networks

J Wang, C Jiang, H Zhang, Y Ren… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …

Deep learning era for future 6G wireless communications—theory, applications, and challenges

SKB Sangeetha, R Dhaya - Artificial intelligent techniques for …, 2022 - Wiley Online Library
Over hundreds of years have passed since wireless communication technology was first
introduced. The developers have made remarkable strides since 1880, including setting up …

When machine learning meets big data: A wireless communication perspective

Y Liu, S Bi, Z Shi, L Hanzo - IEEE Vehicular Technology …, 2019 - ieeexplore.ieee.org
We have witnessed an exponential growth in commercial data services, which has led to the
so-called big data era. Machine learning, one of the most promising artificial intelligence (AI) …

Two applications of deep learning in the physical layer of communication systems

E Björnson, P Giselsson - arXiv preprint arXiv:2001.03350, 2020 - arxiv.org
Deep learning has proved itself to be a powerful tool to develop data-driven signal
processing algorithms for challenging engineering problems. By learning the key features …

Model-aided wireless artificial intelligence: Embedding expert knowledge in deep neural networks for wireless system optimization

A Zappone, M Di Renzo, M Debbah… - IEEE Vehicular …, 2019 - ieeexplore.ieee.org
Deep learning based on artificial neural networks (ANNs) is a powerful machine-learning
method that, in recent years, has been successfully used to realize tasks such as image …

Learning to communicate with autoencoders: Rethinking wireless systems with deep learning

ME Morocho-Cayamcela, JN Njoku… - … in Information and …, 2020 - ieeexplore.ieee.org
The design and implementation of conventional communication systems are based on
strong probabilistic models and assumptions. These fixed and conventional communication …

Deep learning for wireless physical layer: Opportunities and challenges

T Wang, CK Wen, H Wang, F Gao… - China …, 2017 - ieeexplore.ieee.org
Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …

Machine learning in the air

D Gündüz, P de Kerret, ND Sidiropoulos… - IEEE Journal on …, 2019 - ieeexplore.ieee.org
Thanks to the recent advances in processing speed, data acquisition and storage, machine
learning (ML) is penetrating every facet of our lives, and transforming research in many …

Distributed intelligence in wireless networks

X Liu, J Yu, Y Liu, Y Gao, T Mahmoodi… - IEEE Open Journal …, 2023 - ieeexplore.ieee.org
The cloud-based solutions are becoming inefficient due to considerably large time delays,
high power consumption, and security and privacy concerns caused by billions of connected …

Scalable learning paradigms for data-driven wireless communication

Y Xu, F Yin, W Xu, CH Lee, J Lin… - IEEE Communications …, 2020 - ieeexplore.ieee.org
The marriage of wireless big data and machine learning techniques revolutionizes wireless
systems by introducing data-driven philosophy. However, the ever exploding data volume …