Abstract
The goal of the flexible, efficient and fair spectrum allocation is to increase the spectrum utilization of radio resources in future wireless systems. According to the cognitive radio (CR) concept, the nodes are expected to sense their radio environment, take decisions on their operation in the network and learn from their past actions to better adjust to the network dynamics process in various unplanned situations. The CR node can take actions resulting from the input information processing, although in most cases this information is incomplete or inaccurate. CR node may acquire the necessary information needed for its efficient operation by accessing the control or management channel(s) or by interaction with other nodes. Unfortunately, control channels may not always be available and the neighbouring nodes may not be interested in cooperation due to the cost of the spectrum and energy resources. Therefore, the major challenge for a CR node is to operate efficiently with incomplete or limited knowledge on the network and cooperate with its competitors. For CR, game theory provides interesting tools to study competition and cooperation among rational and intelligent players taking decisions with limited or incomplete information. CR nodes can exchange information, cooperate or learn because they were programmed to perform such tasks. Such processes have the associated cost, usually expressed in consumed energy, time or spectrum. These costs must be balanced with benefits. Therefore, the GT models must be carefully selected and evaluated for the application in resource sharing to comprise with practical limitations of the dynamic CR networks. In this work the practical issues of cooperation among cognitive radio nodes competing for available resources in the decentralized networks are considered. It is pondered how the theory of competition and cooperation (game theory) meet the practice, by discussing the quantitative metrics of the cost of avoiding cooperation (the Price of Anarchy—PoA), of having limited knowledge of the competitors (the Price of Ignorance—PoI). Some practical approaches to the spectrum sharing and allocation problem are also presented, which make use of representative, intentionally reduced information that the CR nodes have to exchange. One of the presented methods is based on the repeated game against the network-nodes community using the aggregated knowledge of its possible behavior. The other one is based on the coopetition methodology, which combines the advantages of both cooperative and competitive approaches. It is shown that the problem of radio resource allocation in wireless systems can be solved efficiently by using these not-optimal but practical approaches, by presenting some indicative results: the information-data sum-throughput, Jain’s fairness index, PoA, PoI, and the network welfare function equal to the sum-throughput net.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Note, that these mentioned features of a CR are a subset of abilities mentioned in the definition of intelligence as “a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience” [1]. The quoted document [1] was a public editorial statement signed by fifty-two researchers in fields allied to intelligence testing that claimed to present those findings widely accepted in the expert community.
- 2.
Note, that sometimes PoA is defined using the cost function rather than welfare, i.e. the maximal cost resulting from the non-cooperative game equilibrium over the minimal cost resulting from the cooperation of the players.
- 3.
Note that imperfect information means something else, i.e. the information about the other players’ payoffs or actions is known but may contain an error, that usually has to be accepted if other actions to minimize or eliminate this error are not undertaken.
- 4.
An observation can be made that also in every day life, whenever individuals share limited common resources, e.g. parking places in a city, they act by viewing the rest of the community as a whole, and play against this community, e.g. against other drivers willing to occupy available parking lots, and do not consider each individual other player and her possible payoff.
- 5.
The users can access the channel randomly, use the mechanism similar to 802.11 or a mechanism based on the token exchange. Please, note that it is assumed that users can detect collisions and react for them.
- 6.
- 7.
This approach is similar to Parliamentary Games, in which after elections, the players have some assets (sits in the parliament), and form coalitions to distribute resources (government positions).
References
Editorial: Mainstream science on intelligence. Intelligence 24(1), 13–23 1997 [also in Wall Street Journal on December 13, 1994]
Straffin, P.: Game Theory and Strategy. The Mathematical Association of America, Washington (2002)
Rasmusen, E.: Games and Information: An Introduction to Game Theory. Blackwell Publishers, Oxford (2006)
Watson, J.: Strategy Introduction to Game Theory. W.W. Norton & Company, New York (2002)
MacKenzie, A., DaSaliva, L.: Game Theory for Wireless Engineers. Morgan & Claypool Publishers, California (2006)
Lasaulce, S., Tembine, H.: Game Theory and Learning for Wireless Networks: Fundamentals and Applications. Academic Press, Boston (2011)
Koutsoupias, E., Papadimitriou, C.: Worst-case equilibria. In: Proceedings of the 16th Symposium on Theoretical Aspects of Computer Science, 1999
Komali, R.S., Thomas, R.W., DaSiva, L.A.: The price of ignorance: distributed topology control in cognitive networks. IEEE Trans. Wireless Commun. 9(4), 1434–1445 (2010)
Ratliff, J.: A folk theorem sampler. Great Introductory Notes to the Folk Theorem (1996)
Jain, R., Chiu, D.M., Hawe, W.: A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems. DEC Research, Report TR-301 (1984)
Han, Z., Ji, Z., Liu, K.J.R.: Fair multiuser channel allocation for OFDMA networks using nash bargaining solutions and coalitions. IEEE Trans. Commun. 53(8), 1366–1376 (2005)
Rodrigues, E.B., Casadevall, F.: Control of the trade-off between resource efficiency and user fairness in wireless networks using utility-based adaptive resource allocation. IEEE Commun. Mag. 9, 90–98 (2011)
Capozzi, F., Piro, G., Grieco, L., Boggia, G., Camarda, P.: Downlink packet scheduling in LTE cellular networks: key design issues and a survey. IEEE Commun. Surv. Tutorials 99, 1–23 (2012)
Perlaza, S.M., Debbah, M., Lasaulce, S., Bogucka, H.: On the benefits of bandwidth limiting in decentralized vector multiple access channels. In: Proceedings of 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Germany (2009)
Bogucka, H.: Optimal resource pricing coercing social behavior in wireless networks. In: Proceedings of the IEEE International Communications Conference (ICC), Kyoto (2011)
Brandenburger, A.M., Nalebuff, B.J.: Co-opetition: A Revolutionary Mindset That Combines Competition and Co-operation: The Game Theory Strategy Thats Changing the Game of Business. Doubleday Publication Press, New York (1996)
Nortel: Effective SIR computation for OFDM system-level simulations. TSG-RAN WG1 35, R03–1370 (2003)
Kliks, A., Zalonis, A., Dagres, I., Polydoros, A., Bogucka, H.: PHY abstraction methods for OFDM and NOFDM systems. J.Telecommun. Inf. Technol. 3, 116–122 (2009)
Niyato, D., Hossain, E.: Microeconomic models for dynamic spectrum management in cognitive radio networks. Microeconomic models for dynamic spectrum management in cognitive radio networks. Springer, New York (2007)
Niyato, D., Hossain, E.: A game-theoretic approach to competitive spectrum sharing in cognitive radio networks. In: Proccedings of the IEEE Wireless Communications and Networking Conference, Hong Kong (2007)
Parzy, M., Bogucka, H.: Coopetition practical methodology for efficient sharing of radio resources in wireless networks. In: Proceedings of the 4th International ICST Workshop on Game Theory in Communication Networks (Gamecomm), Paris, France (2011)
Bogucka, H.: Game theoretic model for the OFDM water-filling algorithm with imperfect channel state information. In: IEEE International Communications Conference (ICC) (2008)
Chee, T.K., Lim, C.-C., Choi, J.: A cooperative game theoretic framework for resource allocation in OFDMA systems. In: 10th IEEE Singapore International Conference on Communication systems (ICCS), Singapore (2006)
Acknowledgments
The research leading to these results has received funding from the Polish Ministry of Science and Higher Education, under the grant No. 779/N-COST2010/0 which supports participation in the European COST Action IC0902.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Bogucka, H., Parzy, M. (2014). Cooperation and Competition for Spectrum Sharing in Cognitive Radio Networks: The Practical Perspective. In: Di Benedetto, MG., Bader, F. (eds) Cognitive Communication and Cooperative HetNet Coexistence. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-01402-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-01402-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01401-2
Online ISBN: 978-3-319-01402-9
eBook Packages: EngineeringEngineering (R0)