Enhancing deep reinforcement learning: A tutorial on generative diffusion models in network optimization

H Du, R Zhang, Y Liu, J Wang, Y Lin… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across …

[HTML][HTML] Internet of robotic things for mobile robots: concepts, technologies, challenges, applications, and future directions

H Kabir, ML Tham, YC Chang - Digital Communications and Networks, 2023 - Elsevier
Abstract Nowadays, Multi Robotic System (MRS) consisting of different robot shapes, sizes
and capabilities has received significant attention from researchers and are being deployed …

ED-DQN: An event-driven deep reinforcement learning control method for multi-zone residential buildings

Q Fu, Z Li, Z Ding, J Chen, J Luo, Y Wang, Y Lu - Building and Environment, 2023 - Elsevier
Abstract Residential Heating, Ventilation, and Air conditioning (HVAC) systems are
responsible for a significant amount of energy consumption, but their management is …

Empowering non-terrestrial networks with artificial intelligence: A survey

A Iqbal, ML Tham, YJ Wong, G Wainer, YX Zhu… - IEEE …, 2023 - ieeexplore.ieee.org
6G networks can support global, ubiquitous and seamless connectivity through the
convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios …

ML-based radio resource management in 5G and beyond networks: A survey

IA Bartsiokas, PK Gkonis, DI Kaklamani… - IEEE Access, 2022 - ieeexplore.ieee.org
In this survey, a comprehensive study is provided, regarding the use of machine learning
(ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) …

Advances in improving energy efficiency of fiber–wireless access networks: a comprehensive overview

J Lorincz, Z Klarin, D Begusic - Sensors, 2023 - mdpi.com
Due to the growing impact of the information and communications technology (ICT) sector
on electricity usage and greenhouse gas emissions, telecommunication networks require …

Physics-informed deep reinforcement learning for enhancement on tunnel boring machine's advance speed and stability

P Lin, M Wu, Z **ao, RLK Tiong, L Zhang - Automation in Construction, 2024 - Elsevier
The traditional mode of Tunnel Boring Machine (TBM) operation is limited in their
applicability and efficiency to meet the growing demand for underground spaces. Current …

Towards 6G Technology: Insights into Resource Management for Cloud RAN Deployment

SF Ismail, DJ Kadhim - IoT, 2024 - mdpi.com
Rapid advancements in the development of smart terminals and infrastructure, coupled with
a wide range of applications with complex requirements, are creating traffic demands that …

Aerodynamic optimization of airfoil based on deep reinforcement learning

J Lou, R Chen, J Liu, Y Bao, Y You, Z Chen - Physics of Fluids, 2023 - pubs.aip.org
The traditional optimization of airfoils relies on, and is limited by, the knowledge and
experience of the designer. As a method of intelligent decision-making, reinforcement …

Energy consumption of machine learning enhanced open RAN: A comprehensive review

X Liang, Q Wang, A Al-Tahmeesschi, S Chetty… - IEEE …, 2024 - ieeexplore.ieee.org
The Open Radio Access Network (RAN) emerges as a revolutionary architecture promising
unprecedented levels of openness, flexibility, and intelligence within radio access networks …