Traffic Burst Prediction in Radio Access Network with Machine Learning
Motivated by the expansion of mobile data traffic, there is an increasingdemand for better allocation of radio resources in the radio access network(RAN). Recently, interest has shifted towards predictive resource allocationtechniques, which would enable a more intelligent RAN. A promising solutionf...
Main Author: | Jin, Jing |
---|---|
Format: | Others |
Language: | English |
Published: |
KTH, Skolan för elektro- och systemteknik (EES)
2016
|
Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-197206 |
Similar Items
-
Encrypted Traffic Classification Based on Unsupervised Learning in Cellular Radio Access Networks
by: Carolina Gijon, et al.
Published: (2020-01-01) -
Prediction of Network Traffic Through Light-Weight Machine Learning
by: Yitu Wang, et al.
Published: (2020-01-01) -
Modeling of radio access application protocols for mobile network traffic generation
by: Albasheir, Suliman Kahled
Published: (2008) -
Performance Analysis of Cognitive Radio Networks With Burst Dynamics
by: Qianyu Xu, et al.
Published: (2021-01-01) -
On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
by: Vahid Kouhdaragh, et al.
Published: (2020-04-01)