Beyond deep reinforcement learning: A tutorial on generative diffusion models in network optimization
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a …
Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a …
[HTML][HTML] Internet of robotic things for mobile robots: concepts, technologies, challenges, applications, and future directions
Abstract Nowadays, Multi Robotic System (MRS) consisting of different robot shapes, sizes
and capabilities has received significant attention from researchers and are being deployed …
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 …
responsible for a significant amount of energy consumption, but their management is …
ML-based radio resource management in 5G and beyond networks: A survey
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) …
(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
Due to the growing impact of the information and communications technology (ICT) sector
on electricity usage and greenhouse gas emissions, telecommunication networks require …
on electricity usage and greenhouse gas emissions, telecommunication networks require …
Empowering non-terrestrial networks with artificial intelligence: A survey
6G networks can support global, ubiquitous and seamless connectivity through the
convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios …
convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios …
Aerodynamic optimization of airfoil based on deep reinforcement learning
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 …
experience of the designer. As a method of intelligent decision-making, reinforcement …
A value-added IoT service for cellular networks using federated learning
The number of Internet-of-Things (IoT) devices is expected to reach 64 billion by 2025.
These IoT devices will mostly use cellular networks for transferring a huge amount of IoT …
These IoT devices will mostly use cellular networks for transferring a huge amount of IoT …
Physics-informed deep reinforcement learning for enhancement on tunnel boring machine's advance speed and stability
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 …
applicability and efficiency to meet the growing demand for underground spaces. Current …
Blockchain-based computing resource trading in autonomous multi-access edge network slicing: A dueling double deep Q-learning approach
T Kwantwi, G Sun, NAE Kuadey… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
We investigate the computing resource allocation in multi-access edge network slicing (NS)
in the context of revenue and multi-access edge computing (MEC) resource management …
in the context of revenue and multi-access edge computing (MEC) resource management …