LTE handover parameters optimization using Q-learning technique

Assem Abdelmohsen; Mohamed Abdelwahab; Mohamed Adel; M. Saeed Darweesh; Hassan Mostafa

doi:10.1109/MWSCAS.2018.8623826

LTE handover parameters optimization using Q-learning technique

Assem Abdelmohsen, Mohamed Abdelwahab, Mohamed Adel, M. Saeed Darweesh, Hassan Mostafa

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

23 Scopus citations

Abstract

Optimization of the LTE network is crucial to obtain the best performance. The handover margin (HOM) and time to trigger (TTT) should be chosen so that the system will have minimum number of handovers per user per second, minimum system delay, and maximum throughput. In this paper a new handover optimization algorithm for long term evolution (LTE) network based on Q-learning optimization is presented. The proposed algorithm operates by testing different values of HOM and TTT then observes the output performance corresponding to the values of these parameters, and it eventually selects the values that produce the best performance. The proposed handover optimization technique is evaluated and compared to previous work. Q-learning achieves minimum average number of handover per user and also has maximum throughput than the fuzzy logic optimization technique.

Original language	English (US)
Title of host publication	2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)
Publisher	IEEE
Pages	194-197
Number of pages	4
ISBN (Print)	9781538673928
DOIs	https://doi.org/10.1109/MWSCAS.2018.8623826
State	Published - Jan 24 2019
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-30
Acknowledgements: This research was partially funded by ONE Lab at Cairo University, Zewail City of Science and Technology, and KAUST.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

Access to Document

10.1109/MWSCAS.2018.8623826

Cite this

@inproceedings{3a5410527ac04d9aae8c215d989af311,

title = "LTE handover parameters optimization using Q-learning technique",

abstract = "Optimization of the LTE network is crucial to obtain the best performance. The handover margin (HOM) and time to trigger (TTT) should be chosen so that the system will have minimum number of handovers per user per second, minimum system delay, and maximum throughput. In this paper a new handover optimization algorithm for long term evolution (LTE) network based on Q-learning optimization is presented. The proposed algorithm operates by testing different values of HOM and TTT then observes the output performance corresponding to the values of these parameters, and it eventually selects the values that produce the best performance. The proposed handover optimization technique is evaluated and compared to previous work. Q-learning achieves minimum average number of handover per user and also has maximum throughput than the fuzzy logic optimization technique.",

author = "Assem Abdelmohsen and Mohamed Abdelwahab and Mohamed Adel and {Saeed Darweesh}, M. and Hassan Mostafa",

note = "KAUST Repository Item: Exported on 2022-06-30 Acknowledgements: This research was partially funded by ONE Lab at Cairo University, Zewail City of Science and Technology, and KAUST. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2019",

month = jan,

day = "24",

doi = "10.1109/MWSCAS.2018.8623826",

language = "English (US)",

isbn = "9781538673928",

pages = "194--197",

booktitle = "2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)",

publisher = "IEEE",

}

TY - GEN

T1 - LTE handover parameters optimization using Q-learning technique

AU - Abdelmohsen, Assem

AU - Abdelwahab, Mohamed

AU - Adel, Mohamed

AU - Saeed Darweesh, M.

AU - Mostafa, Hassan

N1 - KAUST Repository Item: Exported on 2022-06-30 Acknowledgements: This research was partially funded by ONE Lab at Cairo University, Zewail City of Science and Technology, and KAUST. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2019/1/24

Y1 - 2019/1/24

N2 - Optimization of the LTE network is crucial to obtain the best performance. The handover margin (HOM) and time to trigger (TTT) should be chosen so that the system will have minimum number of handovers per user per second, minimum system delay, and maximum throughput. In this paper a new handover optimization algorithm for long term evolution (LTE) network based on Q-learning optimization is presented. The proposed algorithm operates by testing different values of HOM and TTT then observes the output performance corresponding to the values of these parameters, and it eventually selects the values that produce the best performance. The proposed handover optimization technique is evaluated and compared to previous work. Q-learning achieves minimum average number of handover per user and also has maximum throughput than the fuzzy logic optimization technique.

AB - Optimization of the LTE network is crucial to obtain the best performance. The handover margin (HOM) and time to trigger (TTT) should be chosen so that the system will have minimum number of handovers per user per second, minimum system delay, and maximum throughput. In this paper a new handover optimization algorithm for long term evolution (LTE) network based on Q-learning optimization is presented. The proposed algorithm operates by testing different values of HOM and TTT then observes the output performance corresponding to the values of these parameters, and it eventually selects the values that produce the best performance. The proposed handover optimization technique is evaluated and compared to previous work. Q-learning achieves minimum average number of handover per user and also has maximum throughput than the fuzzy logic optimization technique.

UR - http://hdl.handle.net/10754/679458

UR - https://ieeexplore.ieee.org/document/8623826/

UR - http://www.scopus.com/inward/record.url?scp=85062215117&partnerID=8YFLogxK

U2 - 10.1109/MWSCAS.2018.8623826

DO - 10.1109/MWSCAS.2018.8623826

M3 - Conference contribution

SN - 9781538673928

SP - 194

EP - 197

BT - 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)

PB - IEEE

ER -

LTE handover parameters optimization using Q-learning technique

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this