Lyapunov-driven deep reinforcement learning for edge inference empowered by reconfigurable intelligent surfaces

K Stylianopoulos, M Merluzzi… - ICASSP 2023-2023 …, 2023 - ieeexplore.ieee.org
In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate
inference at the wireless edge, in the context of 6G networks endowed with reconfigurable …

Harnessing 6G for Consumer-Centric Business Strategies Across Electronic Industries

A Ravisankar, AL Shanthi, S Lavanya… - AI Impacts in Digital …, 2024 - igi-global.com
The advent of 6G technology promises revolutionary advancements in connectivity, offering
not just faster speeds but a paradigm shift in consumer experiences across electronic …

Mobility and privacy-aware offloading of AR applications for healthcare cyber-physical systems in edge computing

K Peng, P Liu, M Bilal, X Xu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Cyber-physical systems (CPSs) can be regarded as a new generation of systems which
have been widely used for healthcare system. The introduction of Augmented Reality (AR) …

Ambient Intelligence assisted fog computing for industrial IoT applications

UM Malik, MA Javed - Computer Communications, 2022 - Elsevier
Abstract Ambient Intelligence (AmI) is a key concept that uses environmental and contextual
information for improving the application experience. The adaptive approach used by AmI …

[HTML][HTML] Handover parameter for self-optimisation in 6G mobile networks: A survey

U Mahamod, H Mohamad, I Shayea, M Othman… - Alexandria Engineering …, 2023 - Elsevier
One of the most crucial issues in mobile networks is ensuring reliable and stable
connectivity during mobility. In recent years, numerous research has examined Fourth …

In-situ model downloading to realize versatile edge AI in 6G mobile networks

K Huang, H Wu, Z Liu, X Qi - IEEE Wireless Communications, 2023 - ieeexplore.ieee.org
The sixth-generation (6G) mobile networks are expected to feature the ubiquitous
deployment of machine learning and artificial intelligence (AI) algorithms at the network …

RIS-assisted over-the-air federated learning in millimeter wave MIMO networks

L Hu, Z Wang, H Zhu, Y Zhou - Journal of Communications and …, 2022 - ieeexplore.ieee.org
In this paper, we propose a reconfigurable intelligent surface (RIS) assisted over-the-air
federated learning (FL), where multiple antennas are deployed at each edge device to …

AI-driven proactive content caching for 6G

G Cheng, C Jiang, B Yue, R Wang… - IEEE Wireless …, 2023 - ieeexplore.ieee.org
To address the limitations of the current proactive content caching technology for the 6th
generation (6G) mobile network, this article comprehensively analyzes the complex …

Message passing meets graph neural networks: A new paradigm for massive MIMO systems

H He, X Yu, J Zhang, S Song… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
As one of the core technologies for 5G systems, massive multiple-input multiple-output
(MIMO) introduces dramatic capacity improvements along with very high beamforming and …

Suspect fault screen assisted graph aggregation network for intra-/inter-node failure localization in ROADM-based optical networks

R Wang, J Zhang, S Yan, C Zeng, H Yu… - Journal of Optical …, 2023 - opg.optica.org
In optical networks, failure localization is essential to stable operation and service
restoration. Several approaches have been presented to achieve accurate failure …