Learning-Based UE Classification in Millimeter-Wave Cellular Systems With Mobility
Pjanić, Dino LU ; Sopasakis, Alexandros LU ; Tataria, Harsh LU ; Tufvesson, Fredrik LU
and Reial, Andres
(2021)
IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
- Abstract
- Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves. For efficient beam tracking it is advantageous to classify users according to their traffic and mobility patterns. Research to date has demonstrated efficient ways of machine learning based UE classification. Although different machine learning approaches have shown success, most of them are based on physical layer attributes of the received signal. This, however, imposes additional complexity and requires access to those lower layer signals. In this paper, we show that traditional supervised and even unsupervised machine learning methods can successfully be applied on higher... (More)
- Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves. For efficient beam tracking it is advantageous to classify users according to their traffic and mobility patterns. Research to date has demonstrated efficient ways of machine learning based UE classification. Although different machine learning approaches have shown success, most of them are based on physical layer attributes of the received signal. This, however, imposes additional complexity and requires access to those lower layer signals. In this paper, we show that traditional supervised and even unsupervised machine learning methods can successfully be applied on higher layer channel measurement reports in order to perform UE classification, thereby reducing the complexity of the classification process. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/67f2e455-6c5b-4fc5-8009-3d993a875284
- author
- Pjanić, Dino
LU
; Sopasakis, Alexandros
LU
; Tataria, Harsh
LU
; Tufvesson, Fredrik
LU
and Reial, Andres
- organization
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
- conference location
- Gold Coast, Australia
- conference dates
- 2021-10-25 - 2021-10-28
- external identifiers
-
- scopus:85122825863
- DOI
- 10.1109/MLSP52302.2021.9596275
- language
- English
- LU publication?
- yes
- id
- 67f2e455-6c5b-4fc5-8009-3d993a875284
- date added to LUP
- 2021-11-15 11:13:55
- date last changed
- 2022-06-29 14:21:39
@inproceedings{67f2e455-6c5b-4fc5-8009-3d993a875284,
abstract = {{Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves. For efficient beam tracking it is advantageous to classify users according to their traffic and mobility patterns. Research to date has demonstrated efficient ways of machine learning based UE classification. Although different machine learning approaches have shown success, most of them are based on physical layer attributes of the received signal. This, however, imposes additional complexity and requires access to those lower layer signals. In this paper, we show that traditional supervised and even unsupervised machine learning methods can successfully be applied on higher layer channel measurement reports in order to perform UE classification, thereby reducing the complexity of the classification process.}},
author = {{Pjanić, Dino and Sopasakis, Alexandros and Tataria, Harsh and Tufvesson, Fredrik and Reial, Andres}},
booktitle = {{IEEE International Workshop on Machine Learning for Signal Processing (MLSP)}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Learning-Based UE Classification in Millimeter-Wave Cellular Systems With Mobility}},
url = {{http://dx.doi.org/10.1109/MLSP52302.2021.9596275}},
doi = {{10.1109/MLSP52302.2021.9596275}},
year = {{2021}},
}