Deep Reinforcement Learning Enabled Energy-Efficient Resource Allocation in Energy Harvesting Aided V2X Communication
Y Song, Y Xiao, Y Chen, G Li… - 2022 IEEE 33rd Annual …, 2022 - ieeexplore.ieee.org
With the commercialization of the 5th generation mobile networks, vehicle-to-everything
(V2X) communication has gained tremendous attention over the last decade. However …
(V2X) communication has gained tremendous attention over the last decade. However …
A DDPG-based Transfer Learning Optimization Framework for User Association and Power Control in HetNet
Z Li, X Wen, Z Lu, W Jing - 2022 IEEE International Conference …, 2022 - ieeexplore.ieee.org
Deep Reinforcement Learning (DRL) has been a useful technique for achieving resource
management in the heterogeneous network (HetNet), such as user association and power …
management in the heterogeneous network (HetNet), such as user association and power …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
Energy efficient ultra-dense network using long short-term memory
The energy consumption of cellular systems is becoming a matter of grave concern in both
economic and environmental perspectives. Recently, in order to reduce the energy …
economic and environmental perspectives. Recently, in order to reduce the energy …
Deep reinforcement learning based resource management in UAV-assisted IoT networks
YY Munaye, RT Juang, HP Lin, GB Tarekegn, DB Lin - Applied Sciences, 2021 - mdpi.com
The resource management in wireless networks with massive Internet of Things (IoT) users
is one of the most crucial issues for the advancement of fifth-generation networks. The main …
is one of the most crucial issues for the advancement of fifth-generation networks. The main …
Semi-centralized optimization for energy efficiency in IoT networks with NOMA
We propose a novel semi-centralized framework for Internet-of-Things (IoT) networks with
non-orthogonal multiple access to maximize the energy efficiency (EE) of two types of …
non-orthogonal multiple access to maximize the energy efficiency (EE) of two types of …
Reinforcement learning-based multiaccess control and battery prediction with energy harvesting in IoT systems
Energy harvesting (EH) is a promising technique to fulfill the long-term and self-sustainable
operations for Internet of Things (IoT) systems. In this paper, we study the joint access …
operations for Internet of Things (IoT) systems. In this paper, we study the joint access …
Multi-Agent Reinforcement Learning-Based Resource Allocation Scheme for UAV-Assisted Internet of Remote Things Systems
Multi-layered communication networks including satellites and unmanned aerial vehicles
(UAVs) with remote sensing capability are expected to be an essential part of next …
(UAVs) with remote sensing capability are expected to be an essential part of next …
Hypergraph based resource-efficient collaborative reinforcement learning for B5G massive IoT
Beyond 5G (B5G) networks rapidly growing to connect billions of Internet of Things (IoT)
devices and the dense deployment of IoT devices leads the large-scale network conflict and …
devices and the dense deployment of IoT devices leads the large-scale network conflict and …
Joint optimization of data offloading and resource allocation with renewable energy aware for IoT devices: A deep reinforcement learning approach
H Ke, J Wang, H Wang, Y Ge - IEEE Access, 2019 - ieeexplore.ieee.org
A large number of connected sensors and devices in Internet of Things (IoT) can generate
large amounts of computing data and increase massive energy consumption. Real-time …
large amounts of computing data and increase massive energy consumption. Real-time …