[HTML][HTML] A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization
Abstract The 5th Generation (5G) and beyond networks are expected to offer huge
throughputs, connect large number of devices, support low latency and large numbers of …
throughputs, connect large number of devices, support low latency and large numbers of …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Resource slicing for eMBB and URLLC services in radio access network using hierarchical deep learning
M Setayesh, S Bahrami… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Network slicing is a promising technique for wireless service providers to support enhanced
mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services …
mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services …
Machine learning in network slicing—a survey
5G and beyond networks are expected to support a wide range of services, with highly
diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the …
diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the …
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] Role of machine learning in resource allocation strategy over vehicular networks: a survey
I Nurcahyani, JW Lee - Sensors, 2021 - mdpi.com
The increasing demand for smart vehicles with many sensing capabilities will escalate data
traffic in vehicular networks. Meanwhile, available network resources are limited. The …
traffic in vehicular networks. Meanwhile, available network resources are limited. The …
Low latency deep learning inference model for distributed intelligent IoT edge clusters
Edge computing is a new paradigm enabling intelligent applications for the Internet of
Things (IoT) using mobile, low-cost IoT devices embedded with data analytics. Due to the …
Things (IoT) using mobile, low-cost IoT devices embedded with data analytics. Due to the …
Deep reinforcement learning for radio resource allocation and management in next generation heterogeneous wireless networks: A survey
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Reinforcement learning for intelligent healthcare systems: A comprehensive survey
The rapid increase in the percentage of chronic disease patients along with the recent
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …
Hierarchical multi-agent DRL-based framework for joint multi-RAT assignment and dynamic resource allocation in next-generation HetNets
This article considers the problem of cost-aware downlink sum-rate maximization via joint
optimal radio access technologies (RATs) assignment and power allocation in next …
optimal radio access technologies (RATs) assignment and power allocation in next …