research-article

Operator Data Driven Cell-Selection in LTE-LAA Coexistence Networks

Authors:

Srikant Manas Kala,

Eitaro Yamatsuta,

Hirozumi Yamaguchi,

Teruo HigashinoAuthors Info & Claims

ICDCN '21: Proceedings of the 22nd International Conference on Distributed Computing and Networking

Pages 206 - 214

https://doi.org/10.1145/3427796.3427818

Published: 05 January 2021 Publication History

Abstract

Efficient cell-selection is essential to realize the gains in network performance promised by LTE Licensed Assisted Access (LAA) in the 5GHz unlicensed band. However, the SINR and transmission power based cell-selection mechanisms employed in LTE HetNets are not suited for LTE-LAA deployments. Further, the impact of cell-association on the performance of the LTE-LAA network and its individual components has not been studied through cellular-operator data. In this work, we address these challenges. We gather a large sample of LTE-LAA deployment data for three cellular operators in the Chicago region, i.e., AT&T, T-Mobile, and Verizon. With the help of operator data, we study the effect of cell-selection on LTE-LAA capacity and network feature relationships through several machine learning techniques. We demonstrate the impact of cell-selection on a combined LTE-LAA system and its licensed and unlicensed components. We show a direct correlation between a cell-quality metric derived from operator data and network performance. Finally, we implement two state-of-the-art cell-association and resource-allocation solutions to show that operator-data-driven cell-selection leads to reduced association time (by as much as 34.89%) and enhanced network capacity (by up to 90.41%).

References

[1]

2020. Network Signal Guru, url=https://play.google.com/ store/apps/details?id=com.qtrun.QuickTest&hl= en_US.

[2]

Ali Abedi and Tim Brecht. 2016. Examining relationships between 802.11 n physical layer transmission feature combinations. In Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. 229–238.

Digital Library

[3]

Samad Ali, Walid Saad, Nandana Rajatheva, Kapseok Chang, Daniel Steinbach, 2020. 6G White Paper on Machine Learning in Wireless Communication Networks.

[4]

Bolin Chen, Jiming Chen, Yuan Gao, and Jie Zhang. 2016. Coexistence of LTE-LAA and Wi-Fi on 5 GHz with corresponding deployment scenarios: A survey. IEEE Communications Surveys & Tutorials 19, 1 (2016), 7–32.

Digital Library

[5]

Ericssion. 2017. T-Mobile, Ericsson exceed 1 Gbps with LAA demo. https://www.ericsson.com/en/press-releases/2017/12/t-mobile-ericsson-exceed-1-gbps-with-laa-demo.

[6]

Dariush Fooladivanda and Catherine Rosenberg. 2019. Joint User Association and Resource Allocation in Heterogeneous Cellular Networks: Comparison of Two Modeling Approaches. In 2019 31st International Teletraffic Congress (ITC 31). IEEE, 66–74.

[7]

Global Mobile Data Traffic Forecast. 2019. Cisco visual networking index: global mobile data traffic forecast update, 2017–2022. Update 2017(2019), 2022.

[8]

GAMS. [n. d.]. General Algebraic Modeling System. Retrieved March 2019 from http://www.gams.com.

[9]

Vincent Huang, Asa Bertze, and Steven Corroy. 2019. Adaptive cell selection in heterogeneous networks. US Patent 10,264,496.

[10]

Srikant Manas Kala, Vanlin Sathya, Seah Winston KG, and Bheemarjuna Reddy Tamma. 2020. CIRNO: Leveraging Capacity Interference Relationship for Dense Networks Optimization. In 2020 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 1–6.

[11]

S. M. Kala, V. Sathya, S. S. Magdam, and B. R. Tamma. 2019. ODiN : Enhancing Resilience of Disaster Networks through Regression Inspired Optimized Routing. In 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). 1–6.

Digital Library

[12]

Furqan Hameed Khan and Marius Portmann. 2019. Joint QoS-control and handover optimization in backhaul aware SDN-based LTE networks. Wireless Networks (2019), 1–23.

[13]

Paulo Valente Klaine, Muhammad Ali Imran, Oluwakayode Onireti, and Richard Demo Souza. 2017. A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Communications Surveys & Tutorials 19, 4 (2017), 2392–2431.

[14]

Toshihito Kudo and Tomoaki Ohtsuki. 2013. Cell range expansion using distributed Q-learning in heterogeneous networks. Eurasip journal on wireless communications and networking 2013, 1(2013), 61.

[15]

Gino Luca Masini and Angelo Centonza. 2016. Neighbor selection for handover in a radio access network. US Patent 9,294,963.

[16]

Ajay Pratap, Rajiv Misra, and Utkarsh Gupta. 2016. Randomized graph coloring algorithm for physical cell id assignment in lte-a femtocellular networks. Wireless Personal Communications 91, 3 (2016), 1213–1235.

Digital Library

[17]

Ajay Pratap, Rishabh Singhal, Rajiv Misra, and Sajal K Das. 2018. Distributed Randomized k-Clustering Based PCID Assignment for Ultra-Dense Femtocellular Networks. IEEE Transactions on Parallel and Distributed Systems 29, 6 (2018), 1247–1260.

[18]

TSG RAN. [n. d.]. Scenarios and requirements for small cell enhancements for E-UTRA and E-UTRAN (Release 12). 3GPP, TR 36([n. d.]), V12.

[19]

Vanlin Sathya, Srikant Manas Kala, S Bhupeshraj, and Bheemarjuna Reddy Tamma. 2020. RAPTAP: a socio-inspired approach to resource allocation and interference management in dense small cells. Wireless Networks (2020), 1–24.

[20]

Vanlin Sathya, Srikant Manas Kala, Muhammad Iqbal Rochman, Monisha Ghosh, and Sumit Roy. 2020. Standardization Advances for Cellular and Wi-Fi Coexistence in the Unlicensed 5 and 6 GHz Bands. GetMobile: Mobile Computing and Communications 24, 1(2020), 5–15.

Digital Library

[21]

Yaohua Sun, Mugen Peng, Yangcheng Zhou, Yuzhe Huang, and Shiwen Mao. 2019. Application of machine learning in wireless networks: Key techniques and open issues. IEEE Communications Surveys & Tutorials 21, 4 (2019), 3072–3108.

Digital Library

[22]

Nessrine Trabelsi, Chung Shue Chen, Rachid El Azouzi, Laurent Roullet, and Eitan Altman. 2017. User association and resource allocation optimization in LTE cellular networks. IEEE Transactions on Network and Service Management 14, 2(2017), 429–440.

Digital Library

[23]

Xuyu Wang, Shiwen Mao, and Michelle X Gong. 2017. A survey of LTE Wi-Fi coexistence in unlicensed bands. GetMobile: Mobile Computing and Communications 20, 3(2017), 17–23.

Digital Library

[24]

Shichang Xu, Ashkan Nikravesh, and Z Morley Mao. 2019. Leveraging Context-Triggered Measurements to Characterize LTE Handover Performance. In International Conference on Passive and Active Network Measurement. Springer, 3–17.

Digital Library

Cited By

Manas Kala SMishra MBiju ASathya VAmano THigashino TYamaguchi HReddy Tamma B(2024)Architecture, Performance, and Usability of Mobile Cellular Network Monitoring Applications for Data-Driven AnalysisIEEE Access10.1109/ACCESS.2024.341275212(88426-88444)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3412752
Kala SMishra MSathya VAmano TGhosh MHigashino TYamaguchi H(2024)Cellular Operator Data Meets Counterfactual Machine LearningIEEE Access10.1109/ACCESS.2024.339431212(64633-64653)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3394312
Kala SSathya VDahiya KHigashino TYamaguchi H(2023)Mitigating Trade-Off in Unlicensed Network Optimization Through Machine Learning and Context AwarenessIEEE Access10.1109/ACCESS.2023.323588211(7873-7891)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3235882
Show More Cited By

Recommendations

LTE Misbehavior Detection in Wi-Fi/LTE Coexistence Under the LAA-LTE Standard
WiSec '18: Proceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks

In this paper, we consider the fair coexistence between LTE and Wi-Fi systems in unlicensed bands. We focus on the misbehavior opportunities that stem from the heterogeneity of the coexisting systems and the lack of explicit coordination mechanisms. We ...
LTE-LAA cell selection through operator data learning and numerosity reduction
Abstract
Long Term Evolution-Licensed Assisted Access (LTE-LAA) architecture is markedly different from traditional LTE HetNets. LTE-LAA deployments also have to contend with interference from coexisting Wi-Fi transmissions in the unlicensed ...
Enhanced network sensitive access control scheme for LTE–LAA/WiFi coexistence: Modeling and performance analysis
Abstract
Implementation of 5G and beyond networks is looking to expand the operation of licensed systems into unlicensed frequency bands through technologies such as license assisted access (LAA) and New Radio Unlicensed (NR-U). LAA aggregates licensed ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICDCN '21: Proceedings of the 22nd International Conference on Distributed Computing and Networking

January 2021

252 pages

ISBN:9781450389334

DOI:10.1145/3427796

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 January 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICDCN '21

ICDCN '21: International Conference on Distributed Computing and Networking 2021

January 5 - 8, 2021

Nara, Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
106
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Manas Kala SMishra MBiju ASathya VAmano THigashino TYamaguchi HReddy Tamma B(2024)Architecture, Performance, and Usability of Mobile Cellular Network Monitoring Applications for Data-Driven AnalysisIEEE Access10.1109/ACCESS.2024.341275212(88426-88444)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3412752
Kala SMishra MSathya VAmano TGhosh MHigashino TYamaguchi H(2024)Cellular Operator Data Meets Counterfactual Machine LearningIEEE Access10.1109/ACCESS.2024.339431212(64633-64653)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3394312
Kala SSathya VDahiya KHigashino TYamaguchi H(2023)Mitigating Trade-Off in Unlicensed Network Optimization Through Machine Learning and Context AwarenessIEEE Access10.1109/ACCESS.2023.323588211(7873-7891)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3235882
Kala SSathya VDahiya KHigashino TYamaguchi H(2022)Identification and Analysis of a Unique Cell Selection Phenomenon in Public Unlicensed Cellular Networks Through Machine LearningIEEE Access10.1109/ACCESS.2022.319940910(87282-87301)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3199409
Kala SSathya VDahiya KHigashino TYamaguchi H(2022)Optimizing Unlicensed Coexistence Network Performance Through Data LearningMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-030-94822-1_8(128-149)Online publication date: 8-Feb-2022
https://doi.org/10.1007/978-3-030-94822-1_8
Kala SSathya VSeah WYamaguchi HHigashino T(2021)Evaluation of Theoretical Interference Estimation Metrics for Dense Wi-Fi Networks2021 International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS51098.2021.9352925(351-359)Online publication date: 5-Jan-2021
https://doi.org/10.1109/COMSNETS51098.2021.9352925

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents