On Spectrum Selection Games in Cognitive Radio Networks

1. On Spectrum Selection Gamesin Cognitive Radio Networks Ilaria Malanchini, Matteo Cesana, Nicola Gatti Dipartimento di Elettronica e Informazione Politecnico di Milano, Milan, Italy

2. Summary • Introduction • Cognitive Radio Networks • Goals and Contributions • Spectrum Selection in Cognitive Networks • The static game model • Dynamic spectrum management • Formulation to solve the games • Experimental evaluation • Conclusion and Future Work

3. Cognitive Radio Networks • Cognitive Radio Networks (CRNs) are a viable solution to solve spectrum efficiency problems by an opportunistic access to the licensed bands • The “holes” in the radio spectrum may be exploited for use by wireless users (secondary users) other than the spectrum licensee (primary users) • CRNs are based on cognitive devices which are able to configure their transmission parameters on the fly depending on the surrounding environment

4. Cognitive Capabilities • Secondary users will be able to exploit the spectrum “holes” using the cognitive radio technology, that allows to: • detect unused spectrum portions (spectrum sensing) • characterize them on the basis of several parameters (spectrum decision) • coordinate with other users in the access phase (spectrum sharing) • handover towards other holes when licensed users appear or if a better opportunity becomes available (spectrum mobility)

5. Goals • Goals: • Evaluation of the spectrum management functionalities • Comparison of different quality measures for the evaluation of the spectrum opportunities • Interaction among secondary users • Analysis of the dynamic evolution of this scenario

6. Contributions • Contributions: • Non-cooperative game theoretic framework that accounts for: • availability/quality of the spectrum portions (s. decision) • interference among secondary users (s. sharing) • cost associated to spectrum handover (s. mobility) • Static analysis • Dynamic analysis

7. Scenario Secondary Interference Range Inactive Primary Users Secondary Users Primary Interference Range Active Primary Users

8. Spectrum Selection Game Model • Players: secondary users • Strategies: available spectrum opportunities (SOPs) • Cost function: we define different cost functions that depend on the number of interferers, the achievable bandwidth and the expected holding time SOP1 (W1,T1) SOP2 (W2,T2) SOP3 (W3,T3) Spectrum occupied by primary users Spectrum opportunities available for secondary users

9. Spectrum Selection Game Model • Spectrum Selection Game (SSG) can be defined: • The generic user i selfishly plays the strategy: • SSG belongs to the class of congestion games • It always admits at least one pure-strategy Nash equilibrium

10. Static Analysis • Interference-based cost function • Linear combination cost function • Product-based cost function

11. Dynamic Spectrum Management • Primary activity is time-varying • The subset of SOPs available for each user can change • We consider a repeated game B SOP(T3W3) SOP(T1W1) SOP (T2W2) T Spectrum occupied by primary users Spectrum opportunities available for secondary users

12. The Multi-Stage Game • Time is divided in epochs which can be defined as the time period where primary activity does not change • At each epoch users play the previous game, but using the following cost function: where K represents the switching cost that a user has to pay if it decides to change the spectrum opportunity • Experimental evaluation aims at comparing the optimal solution and the equilibrium reached by selfish users

13. Solving the games • General model to characterize best/worst Nash equilibria and optimal solution in our congestion game • The following model can be used (and linearized) for each one of the presented cost function • Parameters: • Variables:

14. Solving the games • Constraints: • Objective Function:

15. Experimental Setting Low HT High Holding Time 1 2 3 4 5 6 … 18 p Primary Users Activity Inactive Active High Bandwidth Low Bandwidth Low/Medium/High activity (larger p higher primary activity) Low/High Opportunity p>q  low AND p<q  high q

16. Static Evaluation High Bandwidth High Holding Time Low primary Activity

17. Dynamic Evaluation

18. Conclusion and Future Work • We propose a framework to evaluate spectrum management functionalities in CRN, resorting to a game theoretical approach • This allows a SU to characterize different spectrum opportunities, share available bands with other users and evaluate the possibility to move in a new channel • New simulation scenarios • different kind of users • different available information set/cost functions