[HTML][HTML] Machine learning-based zero-touch network and service management: A survey
J Gallego-Madrid, R Sanchez-Iborra, PM Ruiz… - Digital Communications …, 2022 - Elsevier
The exponential growth of mobile applications and services during the last years has
challenged the existing network infrastructures. Consequently, the arrival of multiple …
challenged the existing network infrastructures. Consequently, the arrival of multiple …
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 learning at the mobile edge: Opportunities for 5G networks
M McClellan, C Cervelló-Pastor, S Sallent - Applied Sciences, 2020 - mdpi.com
Mobile edge computing (MEC) within 5G networks brings the power of cloud computing,
storage, and analysis closer to the end user. The increased speeds and reduced delay …
storage, and analysis closer to the end user. The increased speeds and reduced delay …
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 …
Multi-agent reinforcement learning-based resource management for end-to-end network slicing
To meet the explosive growth of mobile traffic, the 5G network is designed to be flexible and
support multi-access edge computing (MEC), thereby improving the end-to-end quality of …
support multi-access edge computing (MEC), thereby improving the end-to-end quality of …
Reinforcement learning approach for resource allocation in humanitarian logistics
When a disaster strikes, it is important to allocate limited disaster relief resources to those in
need. This paper considers the allocation of resources in humanitarian logistics using three …
need. This paper considers the allocation of resources in humanitarian logistics using three …
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 …
Multi-slice privacy-aware traffic forecasting at RAN level: A scalable federated-learning approach
Next-generation mobile networks are expected to meet the requirements of a wide range of
new vertical services. Hence, the network slicing concept has been introduced, in which …
new vertical services. Hence, the network slicing concept has been introduced, in which …
Distributional-utility actor-critic for network slice performance guarantee
Optimizing distributional utilities (such as mitigating performance tails and maximizing risk-
aware objectives) is crucial for online network slice management to meet the diverse …
aware objectives) is crucial for online network slice management to meet the diverse …
Strategic network slicing management in radio access networks
Network slicing might radically change the relations among different actors of the
telecommunications ecosystem, where new players, active in different markets, could benefit …
telecommunications ecosystem, where new players, active in different markets, could benefit …