Zero touch management: A survey of network automation solutions for 5G and 6G networks

E Coronado, R Behravesh… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Mobile networks are facing an unprecedented demand for high-speed connectivity
originating from novel mobile applications and services and, in general, from the adoption …

Cellular traffic prediction with machine learning: A survey

W Jiang - Expert Systems with Applications, 2022 - Elsevier
Cellular networks are important for the success of modern communication systems, which
support billions of mobile users and devices. Powered by artificial intelligence techniques …

6G and beyond: The future of wireless communications systems

IF Akyildiz, A Kak, S Nie - IEEE access, 2020 - ieeexplore.ieee.org
6G and beyond will fulfill the requirements of a fully connected world and provide ubiquitous
wireless connectivity for all. Transformative solutions are expected to drive the surge for …

AI-native network slicing for 6G networks

W Wu, C Zhou, M Li, H Wu, H Zhou… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
With the global rollout of fifth generation (5G) networks, it is necessary to look beyond 5G
and envision 6G networks. 6G networks are expected to have space-air-ground integrated …

AI-assisted network-slicing based next-generation wireless networks

X Shen, J Gao, W Wu, K Lyu, M Li… - IEEE Open Journal …, 2020 - ieeexplore.ieee.org
The integration of communications with different scales, diverse radio access technologies,
and various network resources renders next-generation wireless networks (NGWNs) highly …

Dynamic RAN slicing for service-oriented vehicular networks via constrained learning

W Wu, N Chen, C Zhou, M Li, X Shen… - IEEE Journal on …, 2020 - ieeexplore.ieee.org
In this paper, we investigate a radio access network (RAN) slicing problem for Internet of
vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple …

DeepCog: Optimizing resource provisioning in network slicing with AI-based capacity forecasting

D Bega, M Gramaglia, M Fiore… - IEEE Journal on …, 2019 - ieeexplore.ieee.org
The dynamic management of network resources is both a critical and challenging task in
upcoming multi-tenant mobile networks, which requires allocating capacity to individual …

Intelligent joint network slicing and routing via GCN-powered multi-task deep reinforcement learning

T Dong, Z Zhuang, Q Qi, J Wang, H Sun… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
In 6G mobile systems, network slicing is an emerging technology to support services with
distinct requirements by dividing a common infrastructure into multiple logical networks …

When 5G meets deep learning: a systematic review

GL Santos, PT Endo, D Sadok, J Kelner - Algorithms, 2020 - mdpi.com
This last decade, the amount of data exchanged on the Internet increased by over a
staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This …

Cellular traffic load prediction with LSTM and Gaussian process regression

W Wang, C Zhou, H He, W Wu… - ICC 2020-2020 IEEE …, 2020 - ieeexplore.ieee.org
Accurate cellular traffic load prediction is a pre-requisite for efficient and automatic network
planning and management. Considering diverse users' activities at different locations and …