Reinforcement Learning for Intelligent Healthcare Systems: A Review of Challenges, Applications, and Open Research Issues
The rise of chronic disease patients and the pandemic pose immediate threats to healthcare
expenditure and mortality rates. This calls for transforming healthcare systems away from …
expenditure and mortality rates. This calls for transforming healthcare systems away from …
[HTML][HTML] Energy-efficient joint resource allocation in 5G HetNet using multi-agent parameterized deep reinforcement learning
Small cells are a promising technique to improve the capacity and throughput of future
wireless networks. However, user association and power allocation in heterogeneous …
wireless networks. However, user association and power allocation in heterogeneous …
Spectral efficiency improvement in downlink fog radio access network with deep reinforcement learning-enabled power control
NB Mohamed, MZ Hassan… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Fog radio access network (F-RAN) is a promising architecture that leverages edge
computing and caching to improve devices' latency and quality of service. However …
computing and caching to improve devices' latency and quality of service. However …
Applications of Deep Reinforcement Learning in Wireless Networks-A Recent Review
Deep Reinforcement Learning (DRL) techniques have gained substantial attention in recent
years for future wireless networks. They can overcome the ever-increasing challenges of …
years for future wireless networks. They can overcome the ever-increasing challenges of …
Towards a multi-agent reinforcement learning approach for joint sensing and sharing in cognitive radio networks
K Rapetswa, L Cheng - Intelligent and Converged Networks, 2023 - ieeexplore.ieee.org
The adoption of the Fifth Generation (5G) and beyond 5G networks is driving the demand for
learning approaches that enable users to co-exist harmoniously in a multi-user distributed …
learning approaches that enable users to co-exist harmoniously in a multi-user distributed …
Deep reinforcement learning approach for HAPS user scheduling in massive MIMO communications
In this paper, we devise a deep SARSA reinforcement learning (DSRL) user scheduling
algorithm for a base station (BS) that uses a high-altitude platform station (HAPS) as a …
algorithm for a base station (BS) that uses a high-altitude platform station (HAPS) as a …
Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
Y Zhu, L Sun, J Wang, R Huang, X Jia - Electronics, 2023 - mdpi.com
5th-Generation (5G) and Time-Sensitive Networking (TSN) are regarded as competitive new
technologies for future industrial networks; 5G-TSN collaboration transmission has drawn …
technologies for future industrial networks; 5G-TSN collaboration transmission has drawn …
Platoon Leader Selection, User Association and Resource Allocation on a C-V2X based highway: A Reinforcement Learning Approach
M Farzanullah, T Le-Ngoc - ICC 2023-IEEE International …, 2023 - ieeexplore.ieee.org
We consider the problem of dynamic platoon leader selection, user association, channel
assignment, and power allocation on a cellular vehicle-to-everything (C-V2X) based …
assignment, and power allocation on a cellular vehicle-to-everything (C-V2X) based …
Resource allocation reinforcement learning for quality of service maintenance in cloud-based services
D Hong, DW Kim, OJ Min, Y Shin - … International Conference on …, 2023 - ieeexplore.ieee.org
Recently, in order to improve the service quality of cloud-based services, research on a
reinforcement learning model that predicts an appropriate amount of cloud resources by …
reinforcement learning model that predicts an appropriate amount of cloud resources by …
Deep Reinforcement Learning Based Resource Allocation Approach for Wireless Networks Considering Network Slicing Paradigm
In this paper, we present an approach for resource scheduling in wireless networks based
on the Network Slicing (NS) paradigm using Double Deep Q-Network (DDQN) …
on the Network Slicing (NS) paradigm using Double Deep Q-Network (DDQN) …