skip to main content
10.1145/3127540.3127554acmconferencesArticle/Chapter ViewAbstractPublication PagesmswimConference Proceedingsconference-collections
research-article

Performance Model for 4G/5G Heterogeneous Networks with Different Classes of Users

Published: 21 November 2017 Publication History

Abstract

In this paper, we analyze flow level performance of mobile users in 4G/pre-5G cellular networks such as LTE and LTE-A. To this end, we develop a two-levels model that provides users' performance in a cell and end-to-end performance in the network. At a cell-level, the model is a multi-class PS queue that captures mobility of users between zones of a cell, through a simple mobility model, that is decoupled from the cell model itself, enabling to directly apply the approach to more realistic mobility patterns. At a network-level, the model is a simple Discrete Time Markov Chain that reproduces the routing of mobile users between the different cells of the system. We first show that this model is consistent with known analytical bounds corresponding to a system with either static users or users having an infinite speed. The outcomes of our model confirm that mobility may improve both users and cells performance, and enable to quantify the gain. The model also shows that, while inter-cell mobility balances the load between cells, it does not lead to such improvement of throughput as intra-cell mobility.

References

[1]
3GPP. [n. d.]. 3GPP TS 36.213 V9.2.0 (2010-06): Physical layer procedures. ([n. d.]).
[2]
Nivine Abbas, Thomas Bonald, and Berna Sayrac. 2015. Opportunistic gains of mobility in cellular data networks. In 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2015. IEEE, 315--322.
[3]
Urtzi Ayesta, Martin Erausquin, and Peter Jacko. 2010. A modeling framework for optimizing the flow-level scheduling with time-varying channels. Perfor- mance Evaluation 67, 11 (2010), 1014--1029.
[4]
Bruno Baynat, R-M Indre, Narcisse Nya, Philippe Olivier, and Alain Simonian. 2015. Impact of mobility in dense LTE-A networks with small cells. In IEEE Vehicular Technology Conference (VTC Spring). IEEE, 1--5.
[5]
Bruno Baynat and Narcisse Nya. 2016. Performance Model for 4G/5G Networks Taking into Account Intra-and Inter-Cell Mobility of Users. In Local Computer Networks (LCN), 2016 IEEE 41st Conference on. IEEE, 212--215.
[6]
Paul Bender, Peter Black, MatthewGrob, Roberto Padovani, Nagabhushana Sind- hushyana, and S Viterbi. 2000. CDMA/HDR: a bandwidth efficient high speed wireless data service for nomadic users. IEEE Communications magazine 38, 7 (2000), 70--77.
[7]
Thomas Bonald, Sem Borst, Nidhi Hegde, Matthieu Jonckheere, and Alexandre Proutiere. 2009. Flow-level performance and capacity of wireless networks with user mobility. Queueing Systems (2009).
[8]
Thomas Bonald, Sem Borst, and Alexandre Proutiere. 2005. Inter-cell schedul- ing in wireless data networks. In Wireless Conference 2005-Next Generation Wire- less and Mobile Communications and Services (European Wireless), 11th European. VDE, 1--7.
[9]
Thomas Bonald, Sem C Borst, and Alexandre Proutière. 2004. How mobility impacts the flow-level performance of wireless data systems. In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies. IEEE.
[10]
Thomas Bonald and Alexandre Proutière. 2003. Wireless downlink data chan- nels: user performance and cell dimensioning. In Proceedings of the 9th annual international conference on Mobile computing and networking. ACM, 339--352.
[11]
Thomas Bonald and Alexandre Proutiere. 2011. A queueing analysis of data networks. In Queueing Networks. Springer, 729--765.
[12]
Sem C Borst, Nidhi Hegde, and Alexandre Proutiere. 2009. Mobility-driven scheduling in wireless networks. In INFOCOM 2009. IEEE, 1260--1268.
[13]
Sem C Borst, Alexandre Proutiere, and Nidhi Hegde. 2006. Capacity of Wireless Data Networks with Intra-and Inter-Cell Mobility. In INFOCOM.
[14]
Andrea J Goldsmith and Soon-Ghee Chua. 1998. Adaptive coded modulation for fading channels. IEEE Transactions on Communications (1998).
[15]
Khalil Ibrahimi, Rachid El-Azouzi, Sujit K Samanta, and El-Houssine Bouyakhf. 2009. Adaptive modulation and coding scheme with intra-and inter-cell mobility for hsdpa system. In 2009 Sixth International Conference on Broadband Commu- nications, Networks, and Systems. IEEE, 1--8.
[16]
Anis Jdidi and Tijani Chahed. 2011. Flow-level performance of proportional fairness with hierarchical modulation in OFDMA-based networks. Computer Networks (2011).
[17]
Stefania Sesia, Matthew Baker, and Issam Toufik. 2011. LTE-the UMTS long term evolution: from theory to practice. John Wiley & Sons.
[18]
Chadi Tarhini and Tijani Chahed. 2012. QoS-oriented resource allocation for streaming flows in IEEE802. 16e Mobile WiMAX. Telecommunication Systems 51, 1 (2012), 65--71.
[19]
IWard Whitt. 2007. IEOR 4701: Professor Whitt Lecture Notes, Monday, July 16, 2007 Introduction to Markov Chains. (2007).

Cited By

View all
  • (2021)A vehicular traffic congestion predictor system using Mamdani fuzzy inferenceSYSTEM THEORY, CONTROL AND COMPUTING JOURNAL10.52846/stccj.2021.1.2.271:2(49-57)Online publication date: 31-Dec-2021
  • (2020)The Scalability Analysis of Machine Learning Based Models in Road Traffic Flow PredictionICC 2020 - 2020 IEEE International Conference on Communications (ICC)10.1109/ICC40277.2020.9148964(1-6)Online publication date: Jun-2020

Index Terms

  1. Performance Model for 4G/5G Heterogeneous Networks with Different Classes of Users

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        MSWiM '17: Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems
        November 2017
        340 pages
        ISBN:9781450351621
        DOI:10.1145/3127540
        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 ACM 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]

        Sponsors

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 21 November 2017

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. data traffic
        2. lte/lte-a networks
        3. markov processes
        4. mobility
        5. performance evaluation
        6. processor sharing queues

        Qualifiers

        • Research-article

        Conference

        MSWiM '17
        Sponsor:

        Acceptance Rates

        MSWiM '17 Paper Acceptance Rate 29 of 142 submissions, 20%;
        Overall Acceptance Rate 398 of 1,577 submissions, 25%

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)1
        • Downloads (Last 6 weeks)0
        Reflects downloads up to 08 Mar 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2021)A vehicular traffic congestion predictor system using Mamdani fuzzy inferenceSYSTEM THEORY, CONTROL AND COMPUTING JOURNAL10.52846/stccj.2021.1.2.271:2(49-57)Online publication date: 31-Dec-2021
        • (2020)The Scalability Analysis of Machine Learning Based Models in Road Traffic Flow PredictionICC 2020 - 2020 IEEE International Conference on Communications (ICC)10.1109/ICC40277.2020.9148964(1-6)Online publication date: Jun-2020

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media