Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services

M Xu, H Du, D Niyato, J Kang, Z Xiong… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating,
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …

Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond

F Liu, Y Cui, C Masouros, J Xu, TX Han… - IEEE journal on …, 2022 - ieeexplore.ieee.org
As the standardization of 5G solidifies, researchers are speculating what 6G will be. The
integration of sensing functionality is emerging as a key feature of the 6G Radio Access …

Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing

W Xu, Z Yang, DWK Ng, M Levorato… - IEEE journal of …, 2023 - ieeexplore.ieee.org
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Holistic network virtualization and pervasive network intelligence for 6G

X Shen, J Gao, W Wu, M Li, C Zhou… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …

Resource scheduling in edge computing: A survey

Q Luo, S Hu, C Li, G Li, W Shi - IEEE Communications Surveys …, 2021 - ieeexplore.ieee.org
With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless
networks, the surging demand for data communications and computing calls for the …

A survey on federated learning for resource-constrained IoT devices

A Imteaj, U Thakker, S Wang, J Li… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

6G wireless communications networks: A comprehensive survey

M Alsabah, MA Naser, BM Mahmmod… - Ieee …, 2021 - ieeexplore.ieee.org
The commercial fifth-generation (5G) wireless communications networks have already been
deployed with the aim of providing high data rates. However, the rapid growth in the number …

Evolution of NOMA toward next generation multiple access (NGMA) for 6G

Y Liu, S Zhang, X Mu, Z Ding, R Schober… - IEEE Journal on …, 2022 - ieeexplore.ieee.org
Due to the explosive growth in the number of wireless devices and diverse wireless
services, such as virtual/augmented reality and Internet-of-Everything, next generation …

Task co-offloading for D2D-assisted mobile edge computing in industrial internet of things

X Dai, Z Xiao, H Jiang, M Alazab… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising
paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co …

Wireless powered mobile edge computing networks: A survey

X Wang, J Li, Z Ning, Q Song, L Guo, S Guo… - ACM Computing …, 2023 - dl.acm.org
Wireless Powered Mobile Edge Computing (WPMEC) is an integration of Mobile Edge
Computing (MEC) and Wireless Power Transfer (WPT) technologies, to both improve …