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A stackelberg game for power control and channel allocation in cognitive radio networks
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Source ACM International Conference Proceeding Series; Vol. 321 archive
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools table of contents
Nantes, France
WORKSHOP SESSION: Information theory and power control table of contents
Article No. 4  
Year of Publication: 2007
ISBN:978-963-9799-00-4
Authors
Michael Bloem  University of Illinois, Urbana, IL
Tansu Alpcan  Deutsche Telekom Labs, Ernst-Reuter-Platz 7, Germany
Tamer Başar  University of Illinois, Urbana, IL
Sponsors
SIGSIM: ACM Special Interest Group on Simulation and Modeling
: Create-Net
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
Publisher
Bibliometrics
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ABSTRACT

The ongoing growth in wireless communication continues to increase demand on the frequency spectrum. The current rigid frequency band allocation policy leads to a significant under-utilization of this scarce resource. However, recent policy changes by the Federal Communications Commission (FCC) and research directions suggested by the Defense Advanced Research Projects Agency (DARPA) have been focusing on wireless devices that can adaptively and intelligently adjust their transmission characteristics, which are known as cognitive radios. This paper suggests a game theoretical approach that allows master-slave cognitive radio pairs to update their transmission powers and frequencies simultaneously. This is shown to lead to an exact potential game, for which it is known that a particular update scheme converges to a Nash Equilibrium (NE). Next, a Stackelberg game model is presented for frequency bands where a licensed user has priority over opportunistic cognitive radios. We suggest a modification to the exact potential game discussed earlier that would allow a Stackelberg leader to charge a virtual price for communicating over a licensed channel. We investigate virtual price update algorithms for the leader and prove the convergence of a specific algorithm. Simulations performed in Matlab verify our convergence results and demonstrate the performance gains over alternative algorithms.


REFERENCES

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Collaborative Colleagues:
Michael Bloem: colleagues
Tansu Alpcan: colleagues
Tamer Başar: colleagues