A Comprehensive Survey on Revolutionizing Connectivity Through Artificial Intelligence-Enabled Digital Twin Network in 6G

M Sheraz, TC Chuah, YL Lee, MM Alam, Z Han - IEEE Access, 2024 - ieeexplore.ieee.org
The deployment of 5G has exposed capacity constraints in realizing the key vision of the
Internet of Everything (IoE). Therefore, the researchers are exploring potentials of Digital …

Advanced Deep Learning Models for 6G: Overview, Opportunities and Challenges

L Jiao, Y Shao, L Sun, F Liu, S Yang, W Ma, L Li… - IEEE …, 2024 - ieeexplore.ieee.org
The advent of the sixth generation of mobile communications (6G) ushers in an era of
heightened demand for advanced network intelligence to tackle the challenges of an …

Llm-twin: Mini-giant model-driven beyond 5g digital twin networking framework with semantic secure communication and computation

Y Hong, J Wu, R Morello - Scientific Reports, 2024 - nature.com
Beyond 5G networks provide solutions for next-generation communications, especially
digital twins networks (DTNs) have gained increasing popularity for bridging physical and …

Next-Generation Multiple Access for Integrated Sensing and Communications

Y Liu, T Huang, F Liu, D Ma, W Huangfu… - Proceedings of the …, 2024 - ieeexplore.ieee.org
Integrated sensing and communications (ISAC) has received considerable attention from
both industry and academia. By sharing the spectrum and hardware platform, ISAC …

THz Network Placement and Mobility-Aware Resource Allocation for Indoor Hybrid THz/VLC Wireless Networks

S Aboagye, H Tabassum - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
This paper focuses on the energy and spectral efficient design of an indoor communication
system that leverages terahertz (THz) and visible light communication (VLC). We first …

Toward Explainable Reasoning in 6G: A Proof of Concept Study on Radio Resource Allocation

F Rezazadeh, H Chergui, J Mangues… - IEEE Open Journal …, 2024 - ieeexplore.ieee.org
The move toward artificial intelligence (AI)-native sixth-generation (6G) networks has put
more emphasis on the importance of explainability and trustworthiness in network …

Unsupervised Power Allocation Based on Combination of Edge Aggregated Graph Attention Network with Deep Unfolded WMMSE

H Hu, Z Xie, H Shi, B Liu, H Zhao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
To address the challenge of optimizing power distribution for transmitter-receiver pairs within
ad hoc wireless networks, deep learning techniques have been employed to navigate the …

[HTML][HTML] UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning

Y Liu, X Li, B He, M Gu, W Huangfu - Drones, 2024 - mdpi.com
Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile
aerial platform to collect data in various applications. However, the existing data collection …

Meta-Learning for Wireless Communications: A Survey and a Comparison to GNNs

B Zhao, J Wu, Y Ma, C Yang - IEEE Open Journal of the …, 2024 - ieeexplore.ieee.org
Deep learning has been used for optimizing a multitude of wireless problems. Yet most
existing works assume that training and test samples are drawn from the same distribution …

Diffusion Models as Network Optimizers: Explorations and Analysis

R Liang, B Yang, P Chen, X Li, Y Xue, Z Yu… - arXiv preprint arXiv …, 2024 - arxiv.org
Network optimization is a fundamental challenge in the Internet of Things (IoT) network,
often characterized by complex features that make it difficult to solve these problems …