Chapter 17ecewp.ece.wpi.edu/.../2011/11/crtextbook_ch17.pdf · “Cognitive Radio Communications...

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

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Chapter 17

Auction-based spectrum markets in cognitive radio networks


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Outline Rethinking Spectrum Auctions On-demand Spectrum Auctions Economic-Robust Spectrum Auctions Double Spectrum Auctions for Multi-party Trading Chapter Summary Further Reading


Recent Spectrum Auction Activities

1.Allocatespectrumstaticallyinlong‐term(10years)nationalleases2.Takemonths/yearstocomplete3.Expensive4.Controlledbyincumbents(Verizon,AT&T)


Addressing Inefficient Spectrum Distribution

Legacy wireless providers own the majority of spectrum But cannot fully utilize it

New wireless providers are dying for usable spectrum But have to crowd into

limited unlicensed bands

Market‐basedSpectrumTrading

Sellers

Buyers


Rethinking Spectrum AuctionseBayintheSky

On‐demandspectrumauctions Short‐term,localarea,low‐cost Noneedtopayfor10yearsof

spectrumusageacrosstheentirewest‐coast

Supportsmallplayersandnewmarketentrants

Stimulatefastinnovations

DynamicSpectrumAuctions

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6

2

3

5 4


Why Auctions?

• Auctioneers periodicallyauctionspectrumbasedonuserbids Dynamicallydiscoverprices

basedondemands

• Users requestspectrumwhentheyneedit Matchtrafficdynamics Flexibleandcost‐effective

DynamicSpectrumAuctions

1

6

2

3

5 4


Summary of Challenges

Multi‐unitauctions Multiplewinners Eachassignedwithaportionof

spectrum

Subjecttointerferenceconstraints Combinatorialconstraintsamong

bidders Complexitygrowsexponentiallywith

thenumberofbidders

NP-hard resource allocation problem

NP-hard resource allocation problem

Canwedesignlow‐complexityandyetefficientauctionsolutionsforlargescalesystems?

Large # of bidders

Real-time auctions


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System Overview

PiecewiseLinearPriceDemandbids– acompactandyethighlyexpressive

biddingformat

User Auctioneer

Uniformvs.Discriminatorypricingmodels– tradeoffsbetweenefficiencyand

fairness

Bidding PricingModel

Fastauctionclearingalgorithmsforbothpricing

models

( g)Allocation(clearing)

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1

6 23 4

Howdousersbid?

Howtosetprices?

howtohandlethebidstoefficientlymaximize

revenue?


Fast Auction Clearing

TheproblemisNP‐hardbecause: Pair‐wisecombinatorialinterferenceconstraints

Whatif:converttheinterferenceconstraintsintoasetoflinearconstraints FunctionsofXi:Theamountofspectrum

assignedtobidderi Mustbeasstrictasbefore ReducetheproblemintovariantsofLinear

ProgrammingProblem Candothisinacentralcontroller

Wepropose:Node‐Lconstraints

Original interference constraints


Analytical Bounds

CAUPClearingAlgorithmforUniformPricing

UPOPTCAUP RR 31

)loglog( UnnnO

CADPClearingAlgorithmforDiscriminatoryPricing

DPOPTCADP RnnR

)( 13

polynomial

Revenueefficiency

Complexity

Whentheconflictgraph

isatreeUPOPTCAUP RR DPOPTCADP RR

Theoreticalbounds


As a Result…..

Usinganormaldesktopcomputer:

• Anauctionwith4000bidderstakes90seconds20,000timefasterthantheoptimalsolution

• If<100bidders,only15%revenuedegradationovertheoptimalsolution


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BidderParticipation

FastAuctionClearing

EfficientDynamicSpectrumAuctions


VERITAS: Truthful and Efficient Spectrum Auctions VERITAS‐Allocation:

Bid‐dependentgreedy allocation Bestknownpolynomial‐timechannelallocationschemesaregreedy Enablespatialreuse Withinaprovabledistance(Δ:maxconflictdegree)totheoptimal

auctionefficiency VERITAS‐Pricing:

Chargeeverywinneri,thebidofitscriticalneighborC(i) CriticalNeighbor:Theneighborwhichmakesthenumberofchannels

availablefori dropto0 FindingCriticalNeighborfori

runallocationson{B/bi}(B:setofbids) Ensuretruthfulness


VERITAS Truthfulness• Theorem:VERITASspectrumauctionis

truthful,achievesparetooptimalallocations,andrunsinpolynomialtimeofO(n3k)

• Proofsketch– Monotoneallocations:Ifthebidderwinswithbidb,

italsowinswithb’>bwhenothers’bidsarefixed– Criticalvalue:Givenabid‐setB,acriticalvalueexists

foreveryallocatedbidder– Truthfulness:Ifwechargeeverybidderbyitscritical

value,nobidderhasanincentivetolie


VERITAS Extensions Supportvariousobjectivefunctions

VERITASallocationschemecansortonbroadclassoffunctionsofbids

Theauctioneercancustomizebasedonitsneeds

BiddingFormats RangeFormat:Everybidderispecifiesparameterdi,and

acceptsanynumberofchannelsintherange(0,di) ContiguousFormat:Bidderrequeststhechannelsallocatedto

becontiguous


A Closer Look at VERITAS Revenuecurvenot

monotonicallyincreasingwith#ofchannelsauctioned Effectofthepricingscheme Successfulauctionsrequire

sufficientlevelofcompetition

Enforcecompetition Choosetheproper#ofchannels

toauction

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Choosing the number of channels to be auctioned to maximize revenue


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Enabling Trading by Double Auctions

Sellers Buyers

BidsBids

Double Auctions: Sellers and buyers are

bidders Seller’s bid: the minimal price it

requires to sell a channel Buyer’s bid: the maximal price it

is willing to pay for a channel

Auctioneer as the match maker Select winning buyers and

sellers

Winners & Prices


Need Judicious Auction Designs

Bids

Sellers Buyers

Bids

Need to achieve 3 economic properties Budget balance: Payment to

sellers <= Charge to buyers Individual rationality:

Buyer pays less than its bid Seller receives more than its

bid Truthfulness: bid the true

valuation Need to provide efficient spectrum distribution

$$


Existing Solutions No Longer Apply

Truthfulness

Individual Rationality

Budget Balance

Spectrum Reuse

McAfee’s Double Auction

√ √ √ XVCG Double

Auction √ √ X XExtension of Single-sided

Truthful Auction

X √ √ √

Our Goal √ √ √ √


Design Guidelines Start from the McAfee design: the most popular truthful

double auction design Achieve all three economic properties without spectrum

reuse

Extend McAfee to assign multiple buyers to each single seller Enable spectrum reuse among buyers

Design the procedure judiciously to maintain the three economic properties


McAfee Double Auctions

Achieve budget balance, truthfulness, individual rationality without spectrum reuse

S1

S2

…

Sk-1

Sk

Sk+1

…

Sm

B1

B2

…

Bk-1

Bk

Bk+1

…

Bn

Sellers’ bidsBuyers’ bids

(k-1) winning

buyers, each paying

Bk

≥≥

≥

≤≥

(k-1) winning

sellers, each getting paid

Sk

Sacrifice one transaction


Enabling Spectrum Reuse

Map a group of non-conflicting buyers to one seller

Sellers’ bidsBuyers’ bids

S1

S2

…

Sk-1

Sk

Sk+1

…

Sm

B1

B2

…

Bk-1

Bk

Bk+1

…

Bn

Buyer Group G1

Buyer Group G2

Buyer Group G3

≥≥

≥

≤≥


TRUST: Auction DesignForm buyer

group

Bid-independent

Group Formation

Decide the bid of each buyer group;

Apply McAfee

Buyer group i’s bid =

The lowest bid in group i *

#of bidders in group i

Charge individuals in a winning buyer

group

Uniform pricing within one

winning buyer group

Theorem 1. TRUST is ex-post budget balanced, individual rational, and truthful.


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Chapter 17 Summary Spectrum is not going to be free (most of it) Economics must be integrated into spectrum

distributions Networking problem: on-demand spectrum allocation Economic problem: truthful (economic-robust) design

Existing solutions fail when enabling spectrum reuse Many ongoing efforts to make this happen in practice


References & Further ReadingsPapers discussed in this chapter: S. Gandhi, C. Buragohain, L. Cao, H. Zheng, and S. Suri, “A general framework for wireless spectrum

auctions,” in Proc. of IEEE DySPAN, 2007. X. Zhou, S. Gandhi, S. Suri, and H. Zheng, “eBay in the sky: Strategy-proof wireless spectrum

auctions,” in Proc. of MobiCom, Sept. 2008. X. Zhou and H. Zheng, “TRUST: A general framework for truthful double spectrum auctions,” in Proc. of

INFOCOM, April 2009.

Further readings: S. Olafsson, B. Glower, and M. Nekovee, “Future management of spectrum,” BT Technology Journal,

vol. 25, no. 2, pp. 52–63, 2007. Ofcom, “Spectrum framework review,” June 2004. M. Buddhikot et. al., “Dimsumnet: New directions in wireless networking using coordinated dynamic

spectrum access,” in Proc. of IEEE WoWmoM05, June 2005. T. K. Forde and L. E. Doyle, “A combinatorial clock auction for OFDMA based cognitive wireless

networks,” in Proc. of 3d International Conference on Wireless Pervasive Computing, May 2008. W. Vickery, “Counterspeculation, auctions and competitive sealed tenders,” Journal of Finance, vol. 16,

pp. 8–37, 1961. D. Lehmann, L. O´callaghan, and Y. Shoham, “Truth revelation in approximately efficient combinatorial

auctions,” J. ACM, vol. 49, no. 5, pp. 577–602, 2002. A. Mu’alem and N. Nisan, “Truthful approximation mechanisms for restricted combinatorial auctions:

extended abstract,” in Eighteenth national conference on Artificial intelligence, pp. 379–384, 2002.

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References & Further Readings R. P. McAfee, “A dominant strategy double auction,” Journal of Economic Theory, vol. 56, pp. 434–450, April

1992. P. Subramanian, H. Gupta, S. R. Das, and M. M. Buddhikot, “Fast spectrum allocation in coordinated dynamic

spectrum access based cellular networks,” in Proc. of IEEE DySPAN, November 2007. Spectrum Bridge Inc., http://www.spectrumbridge.com. P. Subramanian, M. Al-Ayyoub, H. Gupta, S. Das, and M. M. Buddhikot, “Near optimal dynamic spectrum

allocation in cellular networks,” in Proc. Of IEEE DySPAN, 2008. Y. Xing, R. Chandramouli, and C. Cordeiro, “Price dynamics in competitive agile spectrum access markets,” IEEE

Journal on Selected Areas in Communications, vol. 25, no. 3, pp. 613–621, 2007. D. Niyato, E. Hossein, and Z. Han, “Dynamics of multiple-seller and multiple-buyer spectrum trading in cognitive

radio networks: A game theoretic modeling approach,” IEEE Transactions on Mobile Computing, vol. 8, no. 8, pp. 1009–1021, 2009.

V. Rodriguez, K. Mossner, and R. Tafazoli, “Auction-based optimal bidding, pricing and service priorities for multi-rate, multi-class CDMA,” in Proc. Of IEEE PIMRIC, pp. 1850–1854, September 2005.

J. Huang, R. Berry, and M. L. Honig, “Auction-based spectrum sharing,” ACM Mobile Networks and Applications, vol. 11, no. 3, pp. 405–618, 2006.

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Chapter 17ecewp.ece.wpi.edu/.../2011/11/crtextbook_ch17.pdf · “Cognitive Radio Communications...

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