Slides

Politecnico di Milano
Advanced Network Technologies Laboratory
On Spectrum Selection Games
in Cognitive Radio Networks
Ilaria Malanchini, Matteo Cesana, Nicola Gatti
Dipartimento di Elettronica e Informazione
Politecnico di Milano, Milan, Italy
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
2
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
Secondary
Interference
Range
Inactive
Primary
Users
Active
Primary
Users
Secondary
Users
Primary
Interference
Range
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Spectrum Selection Game Model
SOP1
SOP2
(W1,T1)
(W2,T2)
SOP3
(W3,T3)
Spectrum occupied by primary users
Spectrum opportunities available for secondary users
 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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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
B
SOP(T1W1)
SOP(T3W3)
SOP (T2W2)
Spectrum occupied by primary users
Spectrum opportunities available for secondary users
T
 Primary activity is time-varying
 The subset of SOPs available for each user can change
 We consider a repeated game
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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
 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:
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Solving the games
 Constraints:
 Objective Function:
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Experimental Setting
Low HT
High Holding Time
1
2 3
4
5 6
…
Primary Users Activity
High Bandwidthactivity
Low Bandwidth
Low/Medium/High
Inactive
(larger p  higher primary activity)
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p
q
Active
Low/High Opportunity
p>q  low AND p<q  high
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Static Evaluation
High Bandwidth
High Holding Time
Low primary Activity
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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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