Abstract
The rapidly growing demand for wireless communication makes efficient power allocation a critical factor in the network’s efficient operation. Power allocation in cellular networks with interference, where users are selfish, has been recently studied by pricing methods. However, pricing methods do not result in efficient/optimal power allocations for such systems for the following reason. Because of interference, the communication between the Base Station (BS) and a given user is affected by that between the BS and all other users. Thus, the power vector consisting of the transmission power in each BS-user link can be viewed as a public good which simultaneously affects the utilities of all the users in the network. It is well known (Mas-Colell et al., Microeconomic Theory, Oxford University Press, London, 2002, Chap. 11.C) that in public good economies, standard efficiency theorems on market equilibrium do not apply and pricing mechanisms do not result in globally optimal allocations. In this paper we study power allocation in the presence of interference for a single cell wireless Code Division Multiple Access (CDMA) network from a game theoretic perspective. We consider a network where each user knows only its own utility and the channel gain from the base station to itself. We formulate the uplink power allocation problem as a public good allocation problem. We present a game form the Nash Equilibria of which yield power allocations that are optimal solutions of the corresponding centralized uplink network.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Mas-Colell, A., Whinston, M. D., & Green, J. R. (2002). Microeconomic theory. London: Oxford University Press.
Famolari, D. G. D., Mandayam, N. B., & Shah, V. (1999). A new framework for power control in wireless data networks: games, utility and pricing. Norwell: Kluwer Academic.
Ji, H., & Huang, C. (1998). Non-cooperative uplink power control in cellular radio systems. Wireless Networks, 4(3), 233–240.
Saraydar, C., Mandayam, N. B., & Goodman, D. J. (2002). Efficient power control via pricing in wireless data networks. IEEE Transactions on Communications, 50(2), 291–303.
Alpcan, T., Basar, T., Srikant, R., & Altman, E. (2002). Cdma uplink power control as a non-cooperative game. Wireless Networks, 8, 659–670.
Saraydar, C., Mandayam, N. B., & Goodman, D. J. (2001). Pricing and power control in a multicell wireless data network. IEEE Journal of Selected Areas in Communication, 19(10), 1883–1892.
Huang, J., Berry, R. A., & Honig, M. L. (2006) Auction-based spectrum sharing. Mobile Networks and Application 11, 405–418.
Liu, P., Honig, M., & Jordan, S. (2000). Forward-link cdma resource allocation based on pricing. In IEEE wireless communications and networking conference, Chicago, IL, Sept. 2000.
Zhou, C., Honig, M., & Jordan, S. (2001). Two-cell power allocation for wireless data based on pricing. In Allerton conference on communication, control and computing (Vol. 13, No. 7, pp. 1176–1188). Monticello, IL, Sept. 2001.
Aumann, R. (1976). Agreeing to disagree. Annals of Statistics, 4(6), 1236–1239.
Washburn, R., & Teneketzis, D. (1984). Asymptotic agreement among communicating decision makers. Stochastics, 13, 103–129.
Lee, J. W., Mazumdar, R. R., & Shroff, N. B. (2005). Downlink power allocation for multi-class cdma wireless networks. IEEE/ACM Transactions on Network, 13(4), 854–867.
Sharma, S., & Teneketzis, D. (2009) An externalities-based decentralized optimal power allocation algorithm for wireless networks. IEEE/ACM Transactions on Networking, 17, 1819–1831.
Sharma, S., & Teneketzis, D. (2008). A game-theoretic approach to decentralized optimal power allocation for cellular networks, Control Group Report, Dept. of EECS, Univ. of Michigan, vol. CGR 08-05.
Maskin, E. (1985). The theory of implementation in Nash equilibrium: a survey. In Hurwicz, L., Schmeidler, D., & Sonnenschein, H. (Eds.), Social goals and social organization: essays in honor of Elisha A. Pazner. Cambridge: Cambridge University Press.
Reichelstein, S., & Reiter, S. (1988). Game forms with minimal message space. Econometrica, 56(3), 661–692.
Groves, T., & Ledyard, J. (1977). Optimal allocation of public goods: a solution to the ‘free rider’ problem. Econometrica, 45, 783–809.
Hurwicz, L. (1979). Outcome functions yielding Walrasian and Lindahl allocations at Nash equilibrium points. Review of Economic studies, 46, 217–225.
Walker, M. (1981). A simple incentive compatible scheme for attaining lindahl allocations. Econometrica, 49, 65–71.
Palfrey, T., & Srivastava, S. (1993) Bayesian implementation. Fundamentals of Pure and Applied Economics, vol. 53. New York: Harwood Academic Publishers.
Reichelstein, S. (1984). Information and incentives in economic organizations, Ph.D. dissertation, Northwestern University, Evanston, IL, June 1984.
Sharma, S., & Teneketzis, D. (2008). A game-theoretic approach to decentralized optimal power allocation for cellular networks. In Proceedings of GameComm 2008, October 20, Athens, Greece.
Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.
Verdu, S. (2003). Multiuser detection. Cambridge: Cambridge University Press.
Sharma, S. (2009). A mechanism design approach to decentralized resource allocation in wireless and large-scale networks: realization and implementation, Ph.D. dissertation, University of Michigan, Ann Arbor.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sharma, S., Teneketzis, D. A game-theoretic approach to decentralized optimal power allocation for cellular networks. Telecommun Syst 47, 65–80 (2011). https://doi.org/10.1007/s11235-010-9302-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11235-010-9302-6