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“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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
Recent Spectrum Auction Activities
1.Allocatespectrumstaticallyinlong‐term(10years)nationalleases2.Takemonths/yearstocomplete3.Expensive4.Controlledbyincumbents(Verizon,AT&T)
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
Rethinking Spectrum AuctionseBayintheSky
On‐demandspectrumauctions Short‐term,localarea,low‐cost Noneedtopayfor10yearsof
spectrumusageacrosstheentirewest‐coast
Supportsmallplayersandnewmarketentrants
Stimulatefastinnovations
DynamicSpectrumAuctions
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3
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“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
Why Auctions?
• Auctioneers periodicallyauctionspectrumbasedonuserbids Dynamicallydiscoverprices
basedondemands
• Users requestspectrumwhentheyneedit Matchtrafficdynamics Flexibleandcost‐effective
DynamicSpectrumAuctions
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6
2
3
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“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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?
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
Fast Auction Clearing
TheproblemisNP‐hardbecause: Pair‐wisecombinatorialinterferenceconstraints
Whatif:converttheinterferenceconstraintsintoasetoflinearconstraints FunctionsofXi:Theamountofspectrum
assignedtobidderi Mustbeasstrictasbefore ReducetheproblemintovariantsofLinear
ProgrammingProblem Candothisinacentralcontroller
Wepropose:Node‐Lconstraints
Original interference constraints
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
Analytical Bounds
CAUPClearingAlgorithmforUniformPricing
UPOPTCAUP RR 31
)loglog( UnnnO
CADPClearingAlgorithmforDiscriminatoryPricing
DPOPTCADP RnnR
)( 13
polynomial
Revenueefficiency
Complexity
Whentheconflictgraph
isatreeUPOPTCAUP RR DPOPTCADP RR
Theoreticalbounds
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
As a Result…..
Usinganormaldesktopcomputer:
• Anauctionwith4000bidderstakes90seconds20,000timefasterthantheoptimalsolution
• If<100bidders,only15%revenuedegradationovertheoptimalsolution
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
BidderParticipation
FastAuctionClearing
EfficientDynamicSpectrumAuctions
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
VERITAS Truthfulness• Theorem:VERITASspectrumauctionis
truthful,achievesparetooptimalallocations,andrunsinpolynomialtimeofO(n3k)
• Proofsketch– Monotoneallocations:Ifthebidderwinswithbidb,
italsowinswithb’>bwhenothers’bidsarefixed– Criticalvalue:Givenabid‐setB,acriticalvalueexists
foreveryallocatedbidder– Truthfulness:Ifwechargeeverybidderbyitscritical
value,nobidderhasanincentivetolie
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
VERITAS Extensions Supportvariousobjectivefunctions
VERITASallocationschemecansortonbroadclassoffunctionsofbids
Theauctioneercancustomizebasedonitsneeds
BiddingFormats RangeFormat:Everybidderispecifiesparameterdi,and
acceptsanynumberofchannelsintherange(0,di) ContiguousFormat:Bidderrequeststhechannelsallocatedto
becontiguous
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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
$$
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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 √ √ √ √
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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
≥≥
≥
≤≥
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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.
“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 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
“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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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“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)
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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