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Anomaly detection in 5G beam propagation

Hellgren, Katarina LU and Tran, Phiphi (2021) EITM01 20211
Department of Electrical and Information Technology
Abstract
Advancements in today's technology has motivated invention of faster mobile communication systems. The fifth generation mobile network, 5G, is the latest version made by the third generation partnership project (3GPP) and expects to both increase connection speed and reduce latency, which eventually will make it applicable in supporting state-of-the art technologies such as virtual reality and self-driving vehicles among others. A major part of the 5G system is beam management, the system that controlls how signals or beams are assigned to user equipments such as phones and computers. Previous projects have looked at how machine learning could be used in order to improve beam management which has worked well. However, noise in the beams... (More)
Advancements in today's technology has motivated invention of faster mobile communication systems. The fifth generation mobile network, 5G, is the latest version made by the third generation partnership project (3GPP) and expects to both increase connection speed and reduce latency, which eventually will make it applicable in supporting state-of-the art technologies such as virtual reality and self-driving vehicles among others. A major part of the 5G system is beam management, the system that controlls how signals or beams are assigned to user equipments such as phones and computers. Previous projects have looked at how machine learning could be used in order to improve beam management which has worked well. However, noise in the beams makes algorithms unreliable at times and signals unstable. The objective of this master's thesis was thus to apply machine learning in order to decrease the noise in the data, improving performance of beam management further. The machine learning algorithm that yielded the best results was a clustering algorithm called Density-based spatial clustering of applications with noise together with K nearest neighbour. The results implied that by applying the machine learning algorithms and hence avoiding noisy data, higher signal power, higher data transfer speed and lower control signalling overhead could be achieved. (Less)
Please use this url to cite or link to this publication:
author
Hellgren, Katarina LU and Tran, Phiphi
supervisor
organization
course
EITM01 20211
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Machine learning, wireless communication, 5G, beam propagation, DBSCAN, KNN, anomaly detection, clustering
report number
LU/LTH-EIT 2021-815
language
English
id
9048897
date added to LUP
2021-06-09 10:04:43
date last changed
2021-06-09 10:04:43
@misc{9048897,
  abstract     = {{Advancements in today's technology has motivated invention of faster mobile communication systems. The fifth generation mobile network, 5G, is the latest version made by the third generation partnership project (3GPP) and expects to both increase connection speed and reduce latency, which eventually will make it applicable in supporting state-of-the art technologies such as virtual reality and self-driving vehicles among others. A major part of the 5G system is beam management, the system that controlls how signals or beams are assigned to user equipments such as phones and computers. Previous projects have looked at how machine learning could be used in order to improve beam management which has worked well. However, noise in the beams makes algorithms unreliable at times and signals unstable. The objective of this master's thesis was thus to apply machine learning in order to decrease the noise in the data, improving performance of beam management further. The machine learning algorithm that yielded the best results was a clustering algorithm called Density-based spatial clustering of applications with noise together with K nearest neighbour. The results implied that by applying the machine learning algorithms and hence avoiding noisy data, higher signal power, higher data transfer speed and lower control signalling overhead could be achieved.}},
  author       = {{Hellgren, Katarina and Tran, Phiphi}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Anomaly detection in 5G beam propagation}},
  year         = {{2021}},
}