Online Spectrum Allocation for Cognitive Cellular Network Supporting Scalable Demands

1. Online Spectrum Allocation for Cognitive Cellular Network Supporting Scalable Demands Jianfei Wang, JinzhaoSu, Wei Wu 2011-6-2

2. Outline • Motivation • Consideration • Algorithm Detail • Evaluation • Conclusion

3. Motivation • To Decrease Client Waiting Time • Online Spectrum Allocation • To Take advantage of scalable property of request • Satisfaction Degree • Satisfaction degree is equal to ratio of allocated bandwidth versus requested bandwidth

4. Consideration • Don’t reclaim client’s resource by force. • Reclaim will increase system’s network cost. • Reclaim will decrease client’s experience. • High Spectrum Utility Rate • The higher is spectrum utility rate in busy time, the better • Low denial of service denial of service Spectrum Utility Rate

5. System Model • Complete graph • Allocation among users of the same base station • Coordinate Network • Users must get Base station’s permission

6. System Assumption • Arrival of users’ request • Possion process • Users’ service time • Exponential distribution • Queuing rules • FCFS • Capacity of the system • Users’s amount must be less than N0

7. State Machine of System r

8. Stabilization of Normal State • According to the assumption, the system in the normal state can be modeled as queueing system, M/M/N0/N0. • We can get the relationship among satisfaction degree , departure rate and arrival rate.

9. Indentify State Transform • Boundary between Normal and Poor, • Boundary between Rich and Normal, • Target Frequency Ratio of Normal State, • From Frequency Utility Ratio’s aspect, the larger is f , the better. • From denial of service’s aspect, the lower is f, the better.

10. Estimation of Arrival Rate • From the pictrure, we can see that the arrival rate has the seasonal propersty. So we use the seasonal arma model to estimate the arrival rate.

11. Estimation of Departure Rate • Maximum likelihood • The Customers who recently depart • The number of Customers who recently depart is • The service time of customer i is • The customers who are active • The number of active customers is • The service time of active customers which has elapsed is

12. Evaluation • Evaluation Data • Wifidog[7] which were collected from a large number of free Wi-Fi hotspots in Canada for three years. • Evaluation Parameters • Target Frequency Utility Ratio of Normal State is 80% • Boundary between Normal and Poor and Boundary between Rich and Normal are derived from training with part of wifidog data.

13. Evaluation Result Active Consumers vs Satisfaction System Free Spectrum Statistics

14. Conclusion • we propose an algorithm for online spectrum allocation for scalable demands in cognitive cellular network. • We introduce a concept of users’ satisfaction degree to make good use of scalable property of demand. • To handle the issues produced by online property, we involve queueing system and estimations of arrival and departure rate to balance the spectrum utilization and future demands. • With theoretical analysis of system, we give the method of calculating parameters of system. • At last we use collections of real wireless data to evaluate our algorithm.

15. Reference • [1]G.E.P.BoxandG.M.Jenkins. Timeseriesanalysis:forecastingand control.PrenticeHallPTRUpperSaddleRiver,NJ,USA,1994. • [2]M.BuddhikotandK.Ryan.Spectrummanagementincoordinateddynamicspectrumaccessbasedcellularnetworks.InProc.ofIEEE DySPAN,pages299–307,2005. • [3]L.Cao,L.Yang,X.Zhou,Z.Zhang,andH.Zheng.Optimus:SINR-DrivenSpectrumDistributionviaConstraintTransformation.In New FrontiersinDynamicSpectrum,2010IEEESymposiumon,pages1–12. IEEE,2010. • [4]X.Cheng,C.Dale,andJ.Liu.Statisticsandsocialnetworkofyoutubevideos.In QualityofService,2008.IWQoS2008.16thInternational workshopon,pages229–238.IEEE,2008. • [5]L.Kleinrock.Queueingsystems,volume1:theory,1975. • [6]A.Lakhina,K.Papagiannaki,M.Crovella,C.Diot,E.D.Kolaczyk,andN.Taft.Structuralanalysisofnetworktrafficflows. ACMSIGMETRICS Performanceevaluationreview,32(1):61–72,2004. • [7]MichaelLenczner,BenoitGrgoire,andFran?oisProulx.CRAWDADtracesetilesansfil/wifidog/session(v.2007-08-27).Downloadedfrom http://crawdad.cs.dartmouth.edu/ilesansfil/wifidog/session,August2007. • [8]J.Sachs,I.Maric,andA.Goldsmith.CognitiveCellularSystemswithintheTVSpectrum.In NewFrontiersinDynamicSpectrum,2010IEEE Symposiumon,pages1–12.IEEE,2010. • [9]T.Schierl,T.Stockhammer,andT.Wiegand.Mobilevideotransmissionusingscalablevideocoding. IEEETransactionsonCircuitsandSystems forVideoTechnology,17(9):1204–1217,2007. • [10]J.Sjoberg,M.Westerlund,andA.Lakaniemi.Q.Xie,”RTPPayloadFormatandFileStorageFormatfortheAdaptiveMulti-Rate(AMR)and AdaptiveMulti-RateWideband(AMR-WB)AudioCodecs.Technical report,RFC4867,April2007. • [11]S.Sodagari,A.Attar,andS.G.Bilen.StrategiestoAchieveTruthfulSpectrumAuctionsforCognitiveRadioNetworksBasedonMechanismDesign.In NewFrontiersinDynamicSpectrum,2010IEEESymposium on,pages1–6.IEEE,2010.

16. Any Questions? Thank you