Machine learning-based routing and wavelength assignment in software-defined optical networks
Recently, machine learning (ML) has attracted the attention of both researchers and
practitioners to address several issues in the optical networking field. This trend has been …
practitioners to address several issues in the optical networking field. This trend has been …
Machine intelligence techniques for next-generation context-aware wireless networks
The next generation wireless networks (ie 5G and beyond), which would be extremely
dynamic and complex due to the ultra-dense deployment of heterogeneous networks …
dynamic and complex due to the ultra-dense deployment of heterogeneous networks …
The development of leak detection model in subsea gas pipeline using machine learning
J Kim, M Chae, J Han, S Park, Y Lee - Journal of Natural Gas Science and …, 2021 - Elsevier
Pipelines are mainly used to transport crude and refined petroleum, such as natural gas,
worldwide. Monitoring pipeline health condition at offshore locations is challenging. Despite …
worldwide. Monitoring pipeline health condition at offshore locations is challenging. Despite …
The challenges of artificial intelligence in wireless networks for the Internet of Things: Exploring opportunities for growth
The Internet of Things (IoT), a term first coined by Ashton in [1], is an extension of network
connectivity to physical devices, such as actuators, sensors, and mobile devices, that are …
connectivity to physical devices, such as actuators, sensors, and mobile devices, that are …
Mode selection and resource allocation in sliced fog radio access networks: A reinforcement learning approach
The mode selection and resource allocation in fog radio access networks (F-RANs) have
been advocated as key techniques to improve spectral and energy efficiency. In this paper …
been advocated as key techniques to improve spectral and energy efficiency. In this paper …
Reinforcement learning-based radio resource control in 5G vehicular network
Y Zhou, F Tang, Y Kawamoto… - IEEE Wireless …, 2019 - ieeexplore.ieee.org
Recently, the number of user equipment with high mobility (such as vehicles) and the high
traffic demand is immensely increasing. To sustaining the traffic demand, Time Division …
traffic demand is immensely increasing. To sustaining the traffic demand, Time Division …
Optimal placement of virtual machines for supporting multiple applications in mobile edge networks
Although mobile edge computing (MEC), as an extension of the cloud computing paradigm
to edge networks, overcomes some obstacles of traditional mobile cloud computing, ie, the …
to edge networks, overcomes some obstacles of traditional mobile cloud computing, ie, the …
Artificial agent: The fusion of artificial intelligence and a mobile agent for energy-efficient traffic control in wireless sensor networks
Applications of wireless sensor networks are blooming for attacking some limits of social
development, among which energy consumption and communication latency are fatal …
development, among which energy consumption and communication latency are fatal …
Deep neural network for resource management in NOMA networks
Resource management plays a crucial role in improving sum rate of non-orthogonal multiple
access (NOMA) networks. However, the traditional resource management methods have …
access (NOMA) networks. However, the traditional resource management methods have …
Two-phase virtual network function selection and chaining algorithm based on deep learning in SDN/NFV-enabled networks
With the advances of Software-Defined Networks (SDN) and Network Function Virtualization
(NFV), Service Function Chain (SFC) has been becoming a popular paradigm to carry and …
(NFV), Service Function Chain (SFC) has been becoming a popular paradigm to carry and …