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

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

Elektronik ve Hab.Müh.Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

2 Atıf (Scopus)

Özet

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.

Tercüme edilen katkı başlığı	Handover Method Based on Time Series Analysis
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9798350343557
DOI'lar	https://doi.org/10.1109/SIU59756.2023.10223764
Yayın durumu	Yayınlandı - 2023
Etkinlik	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 - Istanbul, Türkiye Süre: 5 Tem 2023 → 8 Tem 2023

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Ülke/Bölge	Türkiye
Şehir	Istanbul
Periyot	5/07/23 → 8/07/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

Keywords

deep learning algorithms
handover
reference signal received power
user equipment

Belgeye Erişim

10.1109/SIU59756.2023.10223764

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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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, Türkiye, 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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

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.

KW - deep learning algorithms

KW - handover

KW - reference signal received power

KW - user equipment

UR - https://www.scopus.com/pages/publications/85173456787

U2 - 10.1109/SIU59756.2023.10223764

DO - 10.1109/SIU59756.2023.10223764

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

Y2 - 5 July 2023 through 8 July 2023

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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap