Knowledge-driven deep learning paradigms for wireless network optimization in 6g

R Sun, N Cheng, C Li, F Chen, W Chen - IEEE Network, 2024 - ieeexplore.ieee.org
In the sixth-generation (6G) networks, newly emerging diversified services of massive users
in dynamic network environments are required to be satisfied by multi-dimensional …

Machine learning for large-scale optimization in 6g wireless networks

Y Shi, L Lian, Y Shi, Z Wang, Y Zhou… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …

Deep learning for wireless networking: The next frontier

Y Cheng, B Yin, S Zhang - IEEE Wireless Communications, 2021 - ieeexplore.ieee.org
With the growth of mobile technology in the last decade, wireless networks have become an
integral part of our everyday lives. To meet the increasingly stringent application …

Functional split of in-network deep learning for 6G: A feasibility study

J He, H Wu, X Xiao, R Bassoli… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
In existing mobile network systems, the data plane (DP) is mainly considered a pipeline
consisting of network elements end-to-end forwarding user data traffics. With the rapid …

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 …

Emerging of Machine Learning and Deep Learning Technology: Addressing in Intelligent Wireless Network Optimization

K Hailemariam, G Singh, MK Madata… - 2023 3rd …, 2023 - ieeexplore.ieee.org
Wireless networks have grown into an essential component of modern life as a result of the
proliferation of wireless technology over what has been a decade. The science underlying …

Five facets of 6G: Research challenges and opportunities

LH Shen, KT Feng, L Hanzo - ACM Computing Surveys, 2023 - dl.acm.org
While the fifth-generation systems are being rolled out across the globe, researchers have
turned their attention to the exploration of radical next-generation solutions. At this early …

Wireless network intelligence at the edge

J Park, S Samarakoon, M Bennis… - Proceedings of the …, 2019 - ieeexplore.ieee.org
Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

Empowering Non-Terrestrial Networks with Artificial Intelligence: A Survey

A Iqbal, ML Tham, YJ Wong, G Wainer, YX Zhu… - IEEE …, 2023 - ieeexplore.ieee.org
6G networks can support global, ubiquitous and seamless connectivity through the
convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios …

Topology aware deep learning for wireless network optimization

S Zhang, B Yin, W Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Data-driven machine learning approaches have been proposed to facilitate wireless
network optimization by learning latent knowledge from historical optimization instances …