skip to main content
article

Price war in heterogeneous wireless networks

Published: 01 September 2010 Publication History

Abstract

Wireless users have the opportunity to choose between heterogeneous access modes, such as 3G, WiFi or WiMAX for instance, which operate with different distance ranges. Due to the increasing commercial interest in access networks, those technologies are often managed by competing providers. The goal of this paper is to study the price war occurring in the case of two providers, with one provider operating in a sub-area of the other. A typical example is that of a WiFi operator against a WiMAX one, WiFi being operated in the smaller area. Using a simple model, we discuss how, for fixed prices, (elastic) demand is split among providers, and then characterize the Nash equilibria for the price war. We derive the conditions on provider capacities and coverage areas under which providers share demand on the common area. A striking additional result is that among the Nash equilibria, the one for which providers set the largest price corresponds to the case when the competitive environment does not bring any loss in terms of social welfare with respect to the socially optimal situation: at equilibrium, the overall utility of the system is maximized. The price of stability is one.

References

[1]
IEEE Standard for Information Technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements - Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Std 802.11-2007 (Revision of IEEE Std 802.11-1999), 2007, C1-1184.
[2]
IEEE 802.16-2004, IEEE Standard for Local and Metropolitan Area Networks - Part 16: Air Interface for Fixed Broadband Wireless Access Systems, October, 2004.
[3]
Piggin, R., WiMAX in depth: broadband wireless access. IEEE Communications Engineer. v2 i5. 36-39.
[4]
Osborne, M. and Rubenstein, A., A Course on Game Theory. 1994. MIT Press.
[5]
Wardrop, J., Some theoretical aspects of road traffic research. Proceedings of the Institute of Civil Engineers. v1. 325-378.
[6]
E. Koutsoupias, C. Papadimitriou, Worst-case equilibria, in: Proc. of 16th Annual Symposium on Theoretical Aspects of Computer Science (STACS 1999), Lecture Notes in Computer Science, vol. 1563, 1999, pp. 404-413.
[7]
E. Anshelevich, A. Dasgupta, J. Kleinberg, E. Tardos, T. Wexler, T. Roughgarden, The price of stability for network design with fair cost allocation, in: Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science (FOCS), 2004, pp. 295-304.
[8]
Altman, E., Boulogne, T., El-Azouzi, Jiménez, T. and Wynter, L., A survey on networking games in telecommunications. Computers and Operations Research. v33 i2. 286-311.
[9]
Courcoubetis, C. and Weber, R., Pricing Communication Networks-Economics, Technology and Modelling. 2003. Wiley.
[10]
DaSilva, L., Pricing of QoS-Enabled Networks: a Survey. IEEE Communications Surveys & Tutorials. v3 i2.
[11]
Tuffin, B., Charging the internet without bandwidth reservation: an overview and bibliography of mathematical approaches. Journal of Information Science and Engineering. v19 i5. 765-786.
[12]
Gibbens, R., Mason, R. and Steinberg, R., Internet service classes under competition. IEEE Journal on Selected Areas in Communications. v18 i12. 2490-2498.
[13]
R. El-Azouzi, E. Altman, L. Wynter, Telecommunications network equilibrium with price and quality-of-service characteristics, in: Proc. of 18th International Teletraffic Congress, Berlin, Germany, 2003.
[14]
Z. Liu, L. Wynter, C. Xia, Pricing information services in a competitive market: avoiding price wars, Technical report, INRIA (2002).
[15]
O. Ileri, D. Samardzija, T. Sizer, N. Mandayam, Demand responsive pricing and competitive spectrum allocation via a spectrum server, in: Proc. of IEEE DySpan 2005, 2006.
[16]
M. Felegyhazi, J. Hubaux, Wireless operatores in a shared spectrum, in: Proc. of IEEE INFOCOM, 2006.
[17]
Xing, Y., Chandramouli, R. and Cordeiro, C., Price dynamics in competitive agile spectrum access markets. IEEE Journal on Selected Areas in Communications. v25 i3. 613-621.
[18]
Bernstein, F. and Federgruen, A., A general equilibrium model for industries with price and service competition. Management Science. v52 i6. 868-886.
[19]
M. Manshaei, J. Freudiger, M. Felegyhazi, P. Marbach, J.-P. Hubaux, On wireless social community networks, in: Proc. of IEEE INFOCOM, Phoenix, AZ, USA, 2008.
[20]
A. Zemlianov, G. de Veciana, Cooperation and decision-making in a wireless multi-provider setting, in: Proc. of IEEE INFOCOM, Miami, FL, USA, 2005.
[21]
Acemoglu, D. and Ozdaglar, A., Competition and efficiency in congested markets. Mathematics of Operations Research. v32 i1. 1-31.
[22]
D. Acemoglu, A. Ozdaglar, Price competition in communication networks, in: Proc. of IEEE INFOCOM, Barcelona, Spain, 2006.
[23]
A. Hayrapetyan, E. Tardos, T. Wexler, A network pricing game for selfish traffic, in: Proc. of IEEE PODC, 2006.
[24]
L. He, J. Walrand, Pricing internet services with multiple providers, in: Proc. of the 41st Allerton Conference on Communication, Control and Computing, 2003.
[25]
L. He, J. Walrand, Pricing and revenue sharing strategies for internet service providers, in: Proc. of IEEE INFOCOM, 2005.
[26]
S. Shakkottai, R. Srikant, Economics of network pricing with multiple ISPs, in: Proc. of IEEE INFOCOM 2005, Miami, FL, USA, 2005.
[27]
P. Maillé, B. Tuffin, Analysis of price competition in a slotted resource allocation game, in: Proc. of IEEE INFOCOM 2008, Phoenix, AZ, USA, 2008.
[28]
Marbach, P., Analysis of a static pricing scheme for priority services. IEEE/ACM Transactions on Networking. v12 i2. 312-325.
[29]
Cole, R., Dodis, Y. and Roughgarden, T., How much can taxes help selfish routing?. Journal of Computer and System Sciences. v72 i3. 444-467.
[30]
Perakis, G., The price of anarchy under nonlinear and asymmetric costs. Mathematics of Operations Research. v32 i3. 614-628.
[31]
Aumann, R.J. and Shapley, L.S., Values of Non-Atomic Games. 1974. Princeton University Press.
[32]
Schmeidler, D., Equilibrium points of nonatomic games. Journal of Statistical Physics. v7 i4. 295-300.
[33]
Maillé, P. and Tuffin, B., Optimization of transmission power in competitive wireless networks. In: Reichl, P., Stiller, B., Tuffin, B. (Eds.), Lecture Notes in Computer Science, vol. 5539. Springer Verlag. pp. 2-10.

Cited By

View all
  1. Price war in heterogeneous wireless networks

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image Computer Networks: The International Journal of Computer and Telecommunications Networking
    Computer Networks: The International Journal of Computer and Telecommunications Networking  Volume 54, Issue 13
    September, 2010
    228 pages

    Publisher

    Elsevier North-Holland, Inc.

    United States

    Publication History

    Published: 01 September 2010

    Author Tags

    1. Competition
    2. Game theory
    3. Pricing
    4. Wireless networks

    Qualifiers

    • Article

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 31 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    View options

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media