Advertisement

Robust Optimization in Non-Linear Regression for Speech and Video Quality Prediction in Mobile Multimedia Networks

  • Charalampos N. Pitas
  • Apostolos G. Fertis
  • Athanasios D. Panagopoulos
  • Philip Constantinou
Conference paper
Part of the Operations Research Proceedings book series (ORP)

Abstract

Quality of service (QoS) and quality of experience (QoE) of contemporary mobile communication networks are crucial, complex and correlated.QoS describes network performance while QoE depicts perceptual quality at the user side. A set of key performance indicators (KPIs) describes in details QoS and QoE. Our research is focused specially on mobile speech and video telephony services that are widely provided by commercial UMTS mobile networks. A key point of cellular network planning and optimization is building voice and video quality prediction models. Prediction models have been developed using measurements data collected from live-world UMTS multimedia networks via drive-test measurement campaign. In this paper, we predict quality of mobile services using regression estimates inspired by the paradigm of robust optimization. The robust estimates suggest a weaker dependence than the one suggested by linear regression estimates between the QoS and QoE parameters and connect the strength of the dependence with the accuracy of the data used to compute the estimates.

Keywords

Robust Optimization Receive Signal Strength Indicator Mean Opinion Score Quality Prediction Model Linear Regression Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ben-Tal, A., El Ghaoui, L., Nemirovski, A.: Robust Optimization. Princeton University Press (2009).Google Scholar
  2. 2.
    Bertsimas, D., Sim, M.: Robust Discrete Optimization and Network Flows, Mathematical Programming 98(1-3), 49-71 (2003).CrossRefGoogle Scholar
  3. 3.
    Bertsimas, D., Sim, M.: The price of robustness. Operations Research 52(1), 35-53 (2004).CrossRefGoogle Scholar
  4. 4.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2004).Google Scholar
  5. 5.
    ECC Report 103: UMTS Coverage Measurements. Electronic Comm. Committee (ECC), European Conf. of Postal and Telecomm. Administrations (CEPT), Nice (2007).Google Scholar
  6. 6.
    Malkowski, M., Claßen, D.: Performance of Video Telephony Services in UMTS using Live Measurements and Network Emulation.Wireless Personal Communications, Springer, 46(1), 19-32 (2008).CrossRefGoogle Scholar
  7. 7.
    Goudarzi, M., Sun, L., Ifeachor, E.: PESQ and 3SQM measurement of voice quality over live 3G networks. 8th Int’l Conf. on Measurement of Speech, Audio and Video Quality in Networks (MESAQIN), Prague (2009).Google Scholar
  8. 8.
    Holma, H., Toskala, A.:WCDMA for UMTS - HSPA Evolution and LTE. 4th Ed., JohnWiley & Sons Ltd. (2007).Google Scholar
  9. 9.
    SeDuMi 1.3: A Matlab toolbox for optimization over symmetric cones. Available [On Line] http://sedumi.ie.lehigh.edu/
  10. 10.
    Soyster, A. L.: Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming. Operations Research, 21(5), 1154-1157 (1973).CrossRefGoogle Scholar
  11. 11.
    Sun, L., Ifeachor, E.: Voice Quality Prediction Models and their Applications in VoIP Networks. IEEE Transactions on Multimedia 8(4), 809-820 (2006).CrossRefGoogle Scholar
  12. 12.
    SwissQual AG: Diversity Benchmarker. [On Line]: http://www.swissqual.com/.
  13. 13.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B (Methodological) 57(1), 267-288 (1995).Google Scholar
  14. 14.
    Vlachodimitropoulos, K., Katsaros, E.: Monitoring the end user perceived speech quality using the derivative mean opinion score (MOS) key performance indicator. 18th Annual IEEE Int’l Symp. on Personal, Indoor and Mobile Radio Comm. (PIMRC’07), Athens (2007).Google Scholar
  15. 15.
    Xu, H., Caramanis, C., Mannor, S.: Robust Regression and Lasso. IEEE Transactions on Information Theory 56(7), 3561-3574 (2010).CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Charalampos N. Pitas
    • 1
  • Apostolos G. Fertis
    • 2
  • Athanasios D. Panagopoulos
    • 1
  • Philip Constantinou
    • 1
  1. 1.Mobile Radiocommunications Laboratory, School of Electrical and Computer EngineeringNational Technical University of AthensAthensGreece
  2. 2.Institute for Operations Research, Department of Mathematics (D-MATH)Eidgenössische Technische Hochschule Zürich, (ETH Zürich)ZürichSwitzerland

Personalised recommendations