Machine learning techniques for optical performance monitoring and modulation format identification: A survey
The trade-off between more user bandwidth and quality of service requirements introduces
unprecedented challenges to the next generation smart optical networks. In this regard, the …
unprecedented challenges to the next generation smart optical networks. In this regard, the …
Leveraging deep reinforcement learning for traffic engineering: A survey
After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
URLLC for 5G and beyond: Requirements, enabling incumbent technologies and network intelligence
The tactile internet (TI) is believed to be the prospective advancement of the internet of
things (IoT), comprising human-to-machine and machine-to-machine communication. TI …
things (IoT), comprising human-to-machine and machine-to-machine communication. TI …
Survey on machine learning for traffic-driven service provisioning in optical networks
T Panayiotou, M Michalopoulou… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The unprecedented growth of the global Internet traffic, coupled with the large spatio-
temporal fluctuations that create, to some extent, predictable tidal traffic conditions, are …
temporal fluctuations that create, to some extent, predictable tidal traffic conditions, are …
QR-SDN: Towards reinforcement learning states, actions, and rewards for direct flow routing in software-defined networks
Flow routing can achieve fine-grained network performance optimizations by routing distinct
packet traffic flows over different network paths. While the centralized control of Software …
packet traffic flows over different network paths. While the centralized control of Software …
Machine learning for optical fiber communication systems: An introduction and overview
Optical networks generate a vast amount of diagnostic, control, and performance monitoring
data. When information is extracted from these data, reconfigurable network elements and …
data. When information is extracted from these data, reconfigurable network elements and …
Drl-based deadline-driven advance reservation allocation in eons for cloud–edge computing
The ongoing roll-out of cloud–edge computing and Internet of Things (IoT) has been
simulating the boom of new advance reservation (AR) services, such as bulk-data migration …
simulating the boom of new advance reservation (AR) services, such as bulk-data migration …
GNN-based hierarchical deep reinforcement learning for NFV-oriented online resource orchestration in elastic optical DCIs
Network function virtualization (NFV) in elastic optical datacenter interconnections (EO-
DCIs) enables flexible and timely deployment of network services. However, as the service …
DCIs) enables flexible and timely deployment of network services. However, as the service …
Flexible technologies to increase optical network capacity
Increased global traffic puts tough requirements not just on fiber communications links but
on the entire network. This manifests itself in multiple ways, including how to optimize …
on the entire network. This manifests itself in multiple ways, including how to optimize …
[HTML][HTML] Overview on routing and resource allocation based machine learning in optical networks
For optical networks, routing and resource allocation which considerably determines the
resource efficiency and network capacity is one of the most important works. It has been …
resource efficiency and network capacity is one of the most important works. It has been …