Application of machine learning in wireless networks: Key techniques and open issues
As a key technique for enabling artificial intelligence, machine learning (ML) is capable of
solving complex problems without explicit programming. Motivated by its successful …
solving complex problems without explicit programming. Motivated by its successful …
Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
[PDF][PDF] Big data service architecture: a survey
J Wang, Y Yang, T Wang, RS Sherratt… - Journal of Internet …, 2020 - jit.ndhu.edu.tw
As one of the main development directions in the information field, big data technology can
be applied for data mining, data analysis and data sharing in the massive data, and it …
be applied for data mining, data analysis and data sharing in the massive data, and it …
Smart resource allocation for mobile edge computing: A deep reinforcement learning approach
The development of mobile devices with improving communication and perceptual
capabilities has brought about a proliferation of numerous complex and computation …
capabilities has brought about a proliferation of numerous complex and computation …
A survey of 5G network systems: challenges and machine learning approaches
Abstract 5G cellular networks are expected to be the key infrastructure to deliver the
emerging services. These services bring new requirements and challenges that obstruct the …
emerging services. These services bring new requirements and challenges that obstruct the …
Computation offloading in multi-access edge computing: A multi-task learning approach
Multi-access edge computing (MEC) has already shown great potential in enabling mobile
devices to bear the computation-intensive applications by offloading some computing jobs to …
devices to bear the computation-intensive applications by offloading some computing jobs to …
Modeling and analysis of energy harvesting and smart grid-powered wireless communication networks: A contemporary survey
The advancements in smart power grid and the advocation of “green communications” have
inspired the wireless communication networks to harness energy from ambient …
inspired the wireless communication networks to harness energy from ambient …
Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey
Abstract Machine learning and artificial intelligence techniques have been proven helpful
when pragmatic to a wide range of complex problems and areas such as energy …
when pragmatic to a wide range of complex problems and areas such as energy …
EdgeGYM: a reinforcement learning environment for constraint-aware NFV resource allocation
Optimizing resource allocation in Network Functions Virtualization (NFV) deployment
remains a challenging problem due to the complex interactions between network functions …
remains a challenging problem due to the complex interactions between network functions …
Data-driven hospitals staff and resources allocation using agent-based simulation and deep reinforcement learning
T Lazebnik - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Hospital staff and resources allocation (HSRA) is a critical challenge in healthcare systems,
as it involves balancing the demands of patients, the availability of resources, and the need …
as it involves balancing the demands of patients, the availability of resources, and the need …