Cellular traffic prediction with machine learning: A survey
W Jiang - Expert Systems with Applications, 2022 - Elsevier
Cellular networks are important for the success of modern communication systems, which
support billions of mobile users and devices. Powered by artificial intelligence techniques …
support billions of mobile users and devices. Powered by artificial intelligence techniques …
Forecasting network traffic: A survey and tutorial with open-source comparative evaluation
This paper presents a review of the literature on network traffic prediction, while also serving
as a tutorial to the topic. We examine works based on autoregressive moving average …
as a tutorial to the topic. We examine works based on autoregressive moving average …
Deep learning for B5G open radio access network: Evolution, survey, case studies, and challenges
Open Radio Access Network (O-RAN) alliance was recently launched to devise a new RAN
architecture featuring open, software-driven, virtual, and intelligent radio access architecture …
architecture featuring open, software-driven, virtual, and intelligent radio access architecture …
Federated learning-empowered mobile network management for 5G and beyond networks: From access to core
The fifth generation (5G) and beyond wireless networks are envisioned to provide an
integrated communication and computing platform that will enable multipurpose and …
integrated communication and computing platform that will enable multipurpose and …
Spatial–temporal graph neural network traffic prediction based load balancing with reinforcement learning in cellular networks
Balancing network traffic among base stations poses a primary challenge for mobile
operators because of the escalating demand for enhanced data speeds in large-scale 5G …
operators because of the escalating demand for enhanced data speeds in large-scale 5G …
Network meets chatgpt: Intent autonomous management, control and operation
Telecommunication has undergone significant transformations due to the continuous
advancements in internet technology, mobile devices, competitive pricing, and changing …
advancements in internet technology, mobile devices, competitive pricing, and changing …
Traffic prediction-assisted federated deep reinforcement learning for service migration in digital twins-enabled MEC networks
In Mobile Edge Computing (MEC) networks, dynamic service migration can support service
continuity and reduce user-perceived delay. However, service migration in MEC networks …
continuity and reduce user-perceived delay. However, service migration in MEC networks …
User-centric heterogeneous-action deep reinforcement learning for virtual reality in the metaverse over wireless networks
The Metaverse emerging as maturing technologies are empowering the different facets.
Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the …
Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the …
Safe-NORA: Safe reinforcement learning-based mobile network resource allocation for diverse user demands
As mobile communication technologies advance, mobile networks become increasingly
complex, and user requirements become increasingly diverse. To satisfy the diverse …
complex, and user requirements become increasingly diverse. To satisfy the diverse …
Deep transfer learning across cities for mobile traffic prediction
Precise citywide mobile traffic prediction is of great significance for intelligent network
planning and proactive service provisioning. Current traffic prediction approaches mainly …
planning and proactive service provisioning. Current traffic prediction approaches mainly …