Zaman Serisi Analizi Tabanli Hücreler Arasi Geçiş Yöntemi

Adnan Can Büyükşirin; Efe Mert Çirpar; Murat Başar Kömürcü; Semiha Tedik Basaran

doi:10.1109/SIU59756.2023.10223764

Zaman Serisi Analizi Tabanli Hücreler Arasi Geçiş Yöntemi

Translated title of the contribution: Handover Method Based on Time Series Analysis

Adnan Can Büyükşirin
, Efe Mert Çirpar
, Murat Başar Kömürcü
, Semiha Tedik Basaran

Department of Electronics and Communication Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In order to meet the increasing data traffic demand in 5G and beyond mobile communication systems, it is expected that the number of cells will increase, leading to more frequent occurrences of handover (HO). However, it is predicted that baseline HO procedures may make incorrect decisions in the case of frequent handovers. Therefore, the use of deep learning algorithms in the HO procedure, along with proactive decision mechanisms, is considered to prevent false handover situations. In this study, the HO decision problem is modeled as a time series classification problem using the measured values of the reference signal received power (RSRP). Performance results of CNN, B-DLSTM, and CB-DLSTM deep learning algorithms are obtained using datasets composed of real measurements. When these results are examined, it can be observed that the proposed model enables more accurate HO decisions to be made in advance.

Translated title of the contribution	Handover Method Based on Time Series Analysis
Original language	Turkish
Title of host publication	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350343557
DOIs	https://doi.org/10.1109/SIU59756.2023.10223764
Publication status	Published - 2023
Event	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 - Istanbul, Turkey Duration: 5 Jul 2023 → 8 Jul 2023

Publication series

Name	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Conference

Conference	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Country/Territory	Turkey
City	Istanbul
Period	5/07/23 → 8/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Access to Document

10.1109/SIU59756.2023.10223764

Cite this

Büyükşirin, A. C., Çirpar, E. M., Kömürcü, M. B., & Basaran, S. T. (2023). Zaman Serisi Analizi Tabanli Hücreler Arasi Geçiş Yöntemi. In 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU59756.2023.10223764

Büyükşirin, Adnan Can ; Çirpar, Efe Mert ; Kömürcü, Murat Başar et al. / Zaman Serisi Analizi Tabanli Hücreler Arasi Geçiş Yöntemi. 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023).

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title = "Zaman Serisi Analizi Tabanli H{\"u}creler Arasi Ge{\c c}i{\c s} Y{\"o}ntemi",

abstract = "In order to meet the increasing data traffic demand in 5G and beyond mobile communication systems, it is expected that the number of cells will increase, leading to more frequent occurrences of handover (HO). However, it is predicted that baseline HO procedures may make incorrect decisions in the case of frequent handovers. Therefore, the use of deep learning algorithms in the HO procedure, along with proactive decision mechanisms, is considered to prevent false handover situations. In this study, the HO decision problem is modeled as a time series classification problem using the measured values of the reference signal received power (RSRP). Performance results of CNN, B-DLSTM, and CB-DLSTM deep learning algorithms are obtained using datasets composed of real measurements. When these results are examined, it can be observed that the proposed model enables more accurate HO decisions to be made in advance.",

keywords = "deep learning algorithms, handover, reference signal received power, user equipment",

author = "B{\"u}y{\"u}k{\c s}irin, \{Adnan Can\} and {\c C}irpar, \{Efe Mert\} and K{\"o}m{\"u}rc{\"u}, \{Murat Ba{\c s}ar\} and Basaran, \{Semiha Tedik\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 ; Conference date: 05-07-2023 Through 08-07-2023",

year = "2023",

doi = "10.1109/SIU59756.2023.10223764",

language = "T{\"u}rk{\c c}e",

series = "31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023",

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Büyükşirin, AC, Çirpar, EM, Kömürcü, MB & Basaran, ST 2023, Zaman Serisi Analizi Tabanli Hücreler Arasi Geçiş Yöntemi. in 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Institute of Electrical and Electronics Engineers Inc., 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Istanbul, Turkey, 5/07/23. https://doi.org/10.1109/SIU59756.2023.10223764

Zaman Serisi Analizi Tabanli Hücreler Arasi Geçiş Yöntemi. / Büyükşirin, Adnan Can; Çirpar, Efe Mert; Kömürcü, Murat Başar et al.
31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Zaman Serisi Analizi Tabanli Hücreler Arasi Geçiş Yöntemi

AU - Büyükşirin, Adnan Can

AU - Çirpar, Efe Mert

AU - Kömürcü, Murat Başar

AU - Basaran, Semiha Tedik

PY - 2023

Y1 - 2023

N2 - In order to meet the increasing data traffic demand in 5G and beyond mobile communication systems, it is expected that the number of cells will increase, leading to more frequent occurrences of handover (HO). However, it is predicted that baseline HO procedures may make incorrect decisions in the case of frequent handovers. Therefore, the use of deep learning algorithms in the HO procedure, along with proactive decision mechanisms, is considered to prevent false handover situations. In this study, the HO decision problem is modeled as a time series classification problem using the measured values of the reference signal received power (RSRP). Performance results of CNN, B-DLSTM, and CB-DLSTM deep learning algorithms are obtained using datasets composed of real measurements. When these results are examined, it can be observed that the proposed model enables more accurate HO decisions to be made in advance.

AB - In order to meet the increasing data traffic demand in 5G and beyond mobile communication systems, it is expected that the number of cells will increase, leading to more frequent occurrences of handover (HO). However, it is predicted that baseline HO procedures may make incorrect decisions in the case of frequent handovers. Therefore, the use of deep learning algorithms in the HO procedure, along with proactive decision mechanisms, is considered to prevent false handover situations. In this study, the HO decision problem is modeled as a time series classification problem using the measured values of the reference signal received power (RSRP). Performance results of CNN, B-DLSTM, and CB-DLSTM deep learning algorithms are obtained using datasets composed of real measurements. When these results are examined, it can be observed that the proposed model enables more accurate HO decisions to be made in advance.

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KW - handover

KW - reference signal received power

KW - user equipment

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M3 - Konferans katkısı

AN - SCOPUS:85173456787

T3 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

BT - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

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ER -

Büyükşirin AC, Çirpar EM, Kömürcü MB, Basaran ST. Zaman Serisi Analizi Tabanli Hücreler Arasi Geçiş Yöntemi. In 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023). doi: 10.1109/SIU59756.2023.10223764

Zaman Serisi Analizi Tabanli Hücreler Arasi Geçiş Yöntemi

Abstract

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