A full dive into realizing the edge-enabled metaverse: Visions, enabling technologies, and challenges

M Xu, WC Ng, WYB Lim, J Kang, Z Xiong… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Dubbed “the successor to the mobile Internet”, the concept of the Metaverse has grown in
popularity. While there exist lite versions of the Metaverse today, they are still far from …

Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges

W Yang, H Du, ZQ Liew, WYB Lim… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
With the increasing demand for intelligent services, the sixth-generation (6G) wireless
networks will shift from a traditional architecture that focuses solely on a high transmission …

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 …

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

L Chang, Z Zhang, P Li, S Xi, 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 …

Swarm of UAVs for network management in 6G: A technical review

MA Khan, N Kumar, SAH Mohsan… - … on Network and …, 2022 - ieeexplore.ieee.org
Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G)
networks, which are capable of incorporating autonomous services to swarm of unmanned …

Deep learning-enabled semantic communication systems with task-unaware transmitter and dynamic data

H Zhang, S Shao, M Tao, X Bi… - IEEE Journal on …, 2022 - ieeexplore.ieee.org
Existing deep learning-enabled semantic communication systems often rely on shared
background knowledge between the transmitter and receiver that includes empirical data …

Computation offloading and resource allocation in MEC-enabled integrated aerial-terrestrial vehicular networks: A reinforcement learning approach

N Waqar, SA Hassan, A Mahmood… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
As important services of the future sixth-generation (6G) wireless networks, vehicular
communication and mobile edge computing (MEC) have received considerable interest in …

Semantic communication meets edge intelligence

W Yang, ZQ Liew, WYB Lim, Z Xiong… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
The development of emerging applications, such as autonomous transportation systems, is
expected to result in an explosive growth in mobile data traffic. As the available spectrum …

Differentially private federated learning via reconfigurable intelligent surface

Y Yang, Y Zhou, Y Wu, Y Shi - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Federated learning (FL), as a disruptive machine learning (ML) paradigm, enables the
collaborative training of a global model over decentralized local data sets without sharing …

Graph neural networks for wireless communications: From theory to practice

Y Shen, J Zhang, SH Song… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep learning-based approaches have been developed to solve challenging problems in
wireless communications, leading to promising results. Early attempts adopted neural …