A survey of online data-driven proactive 5G network optimisation using machine learning

B Ma, W Guo, J Zhang - IEEE access, 2020 - ieeexplore.ieee.org
In the fifth-generation (5G) mobile networks, proactive network optimisation plays an
important role in meeting the exponential traffic growth, more stringent service requirements …

From classical to quantum machine learning: Survey on routing optimization in 6G software defined networking

O Bouchmal, B Cimoli, R Stabile… - Frontiers in …, 2023 - frontiersin.org
The sixth generation (6G) of mobile networks will adopt on-demand self-reconfiguration to
fulfill simultaneously stringent key performance indicators and overall optimization of usage …

[PDF][PDF] Empowering the future 5G networks: an AI based approach

A CB, P Sharma - Telecom Business Review, 2017 - academia.edu
The next telecommunications standard, 5G, envisions that the future networks will support
advanced use cases, such as Internet of things while supporting voluminous simultaneous …

AI-assisted E2E Network Slicing for Integrated Sensing and Communication in 6G Networks

MA Hossain, A Xiang, A Kiani… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
In the realm of modern wireless networks, the integration of wireless sensing and
communication systems is pivotal, especially in the context of the forthcoming 6G Internet of …

[图书][B] White Paper on Machine Learning in 6G Wireless Communication Networks: 6G Research Visions, No. 7, 2020

A Samad, W Saad, R Nandana, C Kapseok… - 2020 - diva-portal.org
This white paper discusses various topics, advances, and projections regarding machine
learning (ML) in wireless communications. Sixth generation (6G) wireless communications …

Machine learning for physical layer in 5G and beyond wireless networks: A survey

J Tanveer, A Haider, R Ali, A Kim - Electronics, 2021 - mdpi.com
Fifth-generation (5G) technology will play a vital role in future wireless networks. The
breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where …

Toward reinforcement-learning-based service deployment of 5G mobile edge computing with request-aware scheduling

Y Zhai, T Bao, L Zhu, M Shen, X Du… - IEEE Wireless …, 2020 - ieeexplore.ieee.org
5G wireless network technology will not only significantly increase bandwidth but also
introduce new features such as mMTC and URLLC. However, high request latency will …

AI empowered resource management for future wireless networks

Y Shen, J Zhang, SH Song… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Resource management plays a pivotal role in wireless networks, which, unfortunately, leads
to challenging NP-hard problems. Artificial Intelligence (AI), especially deep learning …

Deep learning meets wireless network optimization: Identify critical links

L Liu, B Yin, S Zhang, X Cao… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
With the superior capability of discovering intricate structure of large data sets, deep learning
has been widely applied in various areas including wireless networking. While existing deep …

Deep reinforcement learning for mobile 5G and beyond: Fundamentals, applications, and challenges

Z Xiong, Y Zhang, D Niyato, R Deng… - IEEE Vehicular …, 2019 - ieeexplore.ieee.org
Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …