Politecnico di MilanoAdvanced Network Technologies Laboratory

On Spectrum Selection Gamesin Cognitive Radio Networks

Ilaria Malanchini, Matteo Cesana, Nicola GattiDipartimento di Elettronica e InformazionePolitecnico di Milano, Milan, Italy

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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

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

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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)

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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

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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

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Scenario

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SecondaryUsers

InactivePrimary

Users

ActivePrimary

Users

PrimaryInterference

Range

SecondaryInterference

Range

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

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SOP1(W1,T1)

SOP2(W2,T2)

SOP3(W3,T3)

Spectrum occupied by primary usersSpectrum opportunities available for secondary users

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

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Static Analysis

Interference-based cost function

Linear combination cost function

Product-based cost function

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Dynamic Spectrum Management

Primary activity is time-varying The subset of SOPs available for each user can change We consider a repeated game

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T

B

SOP(T1W1)

SOP (T2W2)

SOP(T3W3)

Spectrum occupied by primary usersSpectrum opportunities available for secondary users

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

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Solving the games

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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:

Solving the games

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Constraints:

Objective Function:

Experimental Setting

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1 2 3 4 5 6 … 18

High Bandwidth Low Bandwidth

High Holding Time Low HT

Inactive Active

p

qLow/Medium/High activity

(larger p higher primary activity)

Low/High Opportunityp>q low AND p<q high

Primary Users Activity

Static Evaluation

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High BandwidthHigh Holding Time

Low primary Activity

High BandwidthHigh Holding Time

Low primary Activity

Dynamic Evaluation

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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

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