politecnico di milano a dvanced n etwork t echnologies lab oratory on spectrum selection games in...
Post on 20-Dec-2015
214 views
TRANSCRIPT
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
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
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
3
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)
4
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
5
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
6
Scenario
7
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
8
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
9
Static Analysis
Interference-based cost function
Linear combination cost function
Product-based cost function
10
Dynamic Spectrum Management
Primary activity is time-varying The subset of SOPs available for each user can change We consider a repeated game
11
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
12
Solving the games
13
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:
Experimental Setting
15
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
16
High BandwidthHigh Holding Time
Low primary Activity
High BandwidthHigh Holding Time
Low primary Activity
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
18