A survey of device-to-device communications: Research issues and challenges
Device-to-Device (D2D) communication has emerged as a promising technology for
optimizing spectral efficiency in future cellular networks. D2D takes advantage of the …
optimizing spectral efficiency in future cellular networks. D2D takes advantage of the …
Edge computing for smart health: Context-aware approaches, opportunities, and challenges
Improving the efficiency of healthcare systems is a top national interest worldwide. However,
the need to deliver scalable healthcare services to patients while reducing costs is a …
the need to deliver scalable healthcare services to patients while reducing costs is a …
Multi-agent reinforcement learning for network selection and resource allocation in heterogeneous multi-RAT networks
The rapid production of mobile devices along with the wireless applications boom is
continuing to evolve daily. This motivates the exploitation of wireless spectrum using …
continuing to evolve daily. This motivates the exploitation of wireless spectrum using …
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 …
Edge-assisted solutions for IoT-based connected healthcare systems: A literature review
With the rapid growth of edge-assisted solutions in Internet of Things (IoT) networks,
connected healthcare progressively relies on such solutions. This refers to systems in which …
connected healthcare progressively relies on such solutions. This refers to systems in which …
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 …
Deep reinforcement learning for network selection over heterogeneous health systems
Smart health systems improve our quality oflife by integrating diverse information and
technologies into health and medical practices. Such technologies can significantly improve …
technologies into health and medical practices. Such technologies can significantly improve …
Dynamic network slicing and resource allocation for 5g-and-beyond networks
5G networks are designed not only to transport data, but also to process them while
supporting a vast number of services with different key Performance Indicators (KPIs) …
supporting a vast number of services with different key Performance Indicators (KPIs) …
Edge computing for energy-efficient smart health systems: Data and application-specific approaches
There is a worldwide vision for providing high-quality healthcare services to patients.
However, dealing with the growing number of patients and emergency situations poses …
However, dealing with the growing number of patients and emergency situations poses …
Energy-efficient active federated learning on non-iid data
MH Shullary, AA Abdellatif… - 2022 IEEE 65th …, 2022 - ieeexplore.ieee.org
Distributed learning has gained much interest recently due to its ability to exploit the
distributed resources, at end users and network edges, to cooperatively train a global model …
distributed resources, at end users and network edges, to cooperatively train a global model …