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 **ong… - … 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 …

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 …

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 …

Edge artificial intelligence for 6G: Vision, enabling technologies, and applications

KB Letaief, Y Shi, J Lu, J Lu - IEEE journal on selected areas in …, 2021 - ieeexplore.ieee.org
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

6G-enabled edge AI for metaverse: Challenges, methods, and future research directions

L Chang, Z Zhang, P Li, S **, W Guo… - Journal of …, 2022 - ieeexplore.ieee.org
Sixth generation (6G) enabled edge intelligence opens up a new era of Internet of
everything and makes it possible to interconnect people-devices-cloud anytime, anywhere …

Gradient and channel aware dynamic scheduling for over-the-air computation in federated edge learning systems

J Du, B Jiang, C Jiang, Y Shi… - IEEE Journal on Selected …, 2023 - ieeexplore.ieee.org
To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

Enabling intelligent connectivity: A survey of secure isac in 6g networks

X Zhu, J Liu, L Lu, T Zhang, T Qiu… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
The rapid growth of intelligent sensing capabilities and super computation power in 6G
mobile communication systems has facilitated their application in diverse domains such as …

Multiple access techniques for intelligent and multifunctional 6G: Tutorial, survey, and outlook

B Clerckx, Y Mao, Z Yang, M Chen… - Proceedings of the …, 2024 - ieeexplore.ieee.org
Multiple access (MA) is a crucial part of any wireless system and refers to techniques that
make use of the resource dimensions (eg, time, frequency, power, antenna, code, and …

Communication-efficient distributed learning: An overview

X Cao, T Başar, S Diggavi, YC Eldar… - IEEE journal on …, 2023 - ieeexplore.ieee.org
Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Recent progress in reconfigurable and intelligent metasurfaces: A comprehensive review of tuning mechanisms, hardware designs, and applications

Y Saifullah, Y He, A Boag, GM Yang, F Xu - Advanced Science, 2022 - Wiley Online Library
Intelligent metasurfaces have gained significant importance in recent years due to their
ability to dynamically manipulate electromagnetic (EM) waves. Their multifunctional …