Non-cooperative energy efficient power allocation game in D2D communication: A multi-agent deep reinforcement learning approach
Recently, there is the widespread use of mobile devices and sensors, and rapid emergence
of new wireless and networking technologies, such as wireless sensor network, device-to …
of new wireless and networking technologies, such as wireless sensor network, device-to …
Using reinforcement learning to reduce energy consumption of ultra-dense networks with 5G use cases requirements
S Malta, P Pinto, M Fernández-Veiga - IEEE Access, 2023 - ieeexplore.ieee.org
In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively
increase the network capacity due to cell splitting and densification. A Base Station (BS) is a …
increase the network capacity due to cell splitting and densification. A Base Station (BS) is a …
Green deep reinforcement learning for radio resource management: Architecture, algorithm compression, and challenges
Artificial intelligence (AI) heralds a step-change in wireless networks but may also cause
irreversible environmental damage due to its high energy consumption. Here, we address …
irreversible environmental damage due to its high energy consumption. Here, we address …
Recent studies on deep reinforcement learning in RIS-UAV communication networks
Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) technologies
have recently been identified as enablers for future wireless networks. Deep reinforcement …
have recently been identified as enablers for future wireless networks. Deep reinforcement …
Deep learning for wireless networking: The next frontier
With the growth of mobile technology in the last decade, wireless networks have become an
integral part of our everyday lives. To meet the increasingly stringent application …
integral part of our everyday lives. To meet the increasingly stringent application …
Reinforcement learning: theory and applications in hems
O Al-Ani, S Das - Energies, 2022 - mdpi.com
The steep rise in reinforcement learning (RL) in various applications in energy as well as the
penetration of home automation in recent years are the motivation for this article. It surveys …
penetration of home automation in recent years are the motivation for this article. It surveys …
A Review of Research on the Application of Deep Reinforcement Learning in Unmanned Aerial Vehicle Resource Allocation and Trajectory Planning
Y Cai, E Zhang, Y Qi, L Lu - … on Machine Learning, Big Data and …, 2022 - ieeexplore.ieee.org
In recent years, Unmanned Aerial Vehicle (UAV) has played an important role in the field of
wireless communication with its high mobility and high controllability. In this paper, we focus …
wireless communication with its high mobility and high controllability. In this paper, we focus …
Reinforcement learning for task offloading in mobile edge computing for sdn based wireless networks
N Kiran, C Pan, Y Changchuan - 2020 Seventh International …, 2020 - ieeexplore.ieee.org
The explosive growth of the distributed computing resources in mobile edge computing
(MEC) create a necessity to have a reasonable controller to ensure efficient utilization of …
(MEC) create a necessity to have a reasonable controller to ensure efficient utilization of …
Energy-efficient power allocation and user association in heterogeneous networks with deep reinforcement learning
CK Hsieh, KL Chan, FT Chien - Applied Sciences, 2021 - mdpi.com
This paper studies the problem of joint power allocation and user association in wireless
heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based …
heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based …
Deep reinforcement learning for RSMA-based multi-functional wireless networks
The upcoming sixth generation (6G) is expected to support a wide range of applications that
require efficient sensing, accurate localization, and reliable communication capabilities …
require efficient sensing, accurate localization, and reliable communication capabilities …