Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems
Pjanic, Dino LU ; Sopasakis, Alexandros LU ; Reial, Andres and Tufvesson, Fredrik LU
(2024)
In IEEE Open Journal of the Communications Society
5.
- Abstract
The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) systems with large antenna arrays allow resolving finer details of channel behavior, we investigate how machine learning can be applied to time series data of beam measurements in the Fifth Generation (5G) New Radio (NR) system to improve the HO procedure. This paper introduces the Early-Scheduled Handover Preparation scheme designed to enhance the robustness and efficiency of the HO procedure, particularly in scenarios... (More)
The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) systems with large antenna arrays allow resolving finer details of channel behavior, we investigate how machine learning can be applied to time series data of beam measurements in the Fifth Generation (5G) New Radio (NR) system to improve the HO procedure. This paper introduces the Early-Scheduled Handover Preparation scheme designed to enhance the robustness and efficiency of the HO procedure, particularly in scenarios involving high mobility and dense small cell deployments. Early-Scheduled Handover Preparation focuses on optimizing the timing of the HO preparation phase by leveraging machine learning techniques to predict the earliest possible trigger points for HO events. We identify a new early trigger for HO preparation and demonstrate how it can beneficially reduce the required time for HO execution reducing channel quality degradation. These insights enable a new HO preparation scheme that offers a novel, user-aware, and proactive HO decision making in MIMO scenarios incorporating mobility.
(Less)
- author
- Pjanic, Dino
LU
; Sopasakis, Alexandros
LU
; Reial, Andres
and Tufvesson, Fredrik
LU
- organization
-
- Department of Electrical and Information Technology
- LTH Profile Area: Engineering Health
- LU Profile Area: Natural and Artificial Cognition
- LU Profile Area: Nature-based future solutions
- Computer Vision and Machine Learning (research group)
- eSSENCE: The e-Science Collaboration
- Partial differential equations (research group)
- Numerical Analysis and Scientific Computing (research group)
- publishing date
- 2024-10-30
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- beam management, handover control parameters, handover preparation, measurement event A3, ML, mmWave, mobility robustness optimization
- in
- IEEE Open Journal of the Communications Society
- volume
- 5
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85208103011
- ISSN
- 2644-125X
- DOI
- 10.1109/OJCOMS.2024.3488594
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2020 IEEE.
- id
- 821d183e-609c-4f8f-aa12-23561aeba512
- date added to LUP
- 2024-11-14 05:28:10
- date last changed
- 2025-01-14 16:03:35
@article{821d183e-609c-4f8f-aa12-23561aeba512,
abstract = {{<p>The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) systems with large antenna arrays allow resolving finer details of channel behavior, we investigate how machine learning can be applied to time series data of beam measurements in the Fifth Generation (5G) New Radio (NR) system to improve the HO procedure. This paper introduces the Early-Scheduled Handover Preparation scheme designed to enhance the robustness and efficiency of the HO procedure, particularly in scenarios involving high mobility and dense small cell deployments. Early-Scheduled Handover Preparation focuses on optimizing the timing of the HO preparation phase by leveraging machine learning techniques to predict the earliest possible trigger points for HO events. We identify a new early trigger for HO preparation and demonstrate how it can beneficially reduce the required time for HO execution reducing channel quality degradation. These insights enable a new HO preparation scheme that offers a novel, user-aware, and proactive HO decision making in MIMO scenarios incorporating mobility.</p>}},
author = {{Pjanic, Dino and Sopasakis, Alexandros and Reial, Andres and Tufvesson, Fredrik}},
issn = {{2644-125X}},
keywords = {{beam management; handover control parameters; handover preparation; measurement event A3; ML; mmWave; mobility robustness optimization}},
language = {{eng}},
month = {{10}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Open Journal of the Communications Society}},
title = {{Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems}},
url = {{http://dx.doi.org/10.1109/OJCOMS.2024.3488594}},
doi = {{10.1109/OJCOMS.2024.3488594}},
volume = {{5}},
year = {{2024}},
}