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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 1 Chapter 17 Auction-based spectrum markets in cognitive radio networks

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 2 Outline Rethinking Spectrum Auctions On-demand Spectrum Auctions Economic-Robust Spectrum Auctions Double Spectrum Auctions for Multi-party Trading Chapter Summary Further Reading

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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. Allocate spectrum statically in long-term (10 years) national leases 2. Take months/years to complete 3. Expensive 4. Controlled by incumbents (Verizon, AT&T)

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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-based Spectrum Trading Sellers Buyers

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) Rethinking Spectrum Auctions eBay in the Sky On-demand spectrum auctions Short-term, local area, low-cost No need to pay for 10 years of spectrum usage across the entire west-coast Support small players and new market entrants Stimulate fast innovations Dynamic Spectrum Auctions 162354

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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 periodically auction spectrum based on user bids Dynamically discover prices based on demands Users request spectrum when they need it Match traffic dynamics Flexible and cost-effective Dynamic Spectrum Auctions 162354

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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-unit auctions Multiple winners Each assigned with a portion of spectrum Subject to interference constraints Combinatorial constraints among bidders Complexity grows exponentially with the number of bidders NP-hard resource allocation problem Can we design low-complexity and yet efficient auction solutions for large scale systems? Large # of bidders Real-time auctions

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 8 Outline Rethinking Spectrum Auctions On-demand Spectrum Auctions Economic-Robust Spectrum Auctions Double Spectrum Auctions for Multi-party Trading Chapter Summary Further Reading

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) System Overview Piecewise Linear Price Demand bids– a compact and yet highly expressive bidding format UserAuctioneer Uniform vs. Discriminatory pricing models – tradeoffs between efficiency and fairness Bidding Pricing Model Fast auction clearing algorithms for both pricing models Allocation (clearing) 5 1 6 2 34 How do users bid? How to set prices? how to handle the bids to efficiently maximize revenue?

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) Fast Auction Clearing The problem is NP-hard because: Pair-wise combinatorial interference constraints What if: convert the interference constraints into a set of linear constraints Functions of Xi: The amount of spectrum assigned to bidder i Must be as strict as before Reduce the problem into variants of Linear Programming Problem Can do this in a central controller We propose: Node-L constraints Original interference constraints

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) Analytical Bounds CAUP Clearing Algorithm for Uniform Pricing CADP Clearing Algorithm for Discriminatory Pricing Revenue efficiency Complexity When the conflict graph is a tree Theoretical bounds

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) As a Result….. Using a normal desktop computer: An auction with 4000 bidders takes 90 seconds 20,000 time faster than the optimal solution If <100 bidders, only 15% revenue degradation over the optimal solution Using a normal desktop computer: An auction with 4000 bidders takes 90 seconds 20,000 time faster than the optimal solution If <100 bidders, only 15% revenue degradation over the optimal solution

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 13 Outline Rethinking Spectrum Auctions On-demand Spectrum Auctions Economic-Robust Spectrum Auctions Double Spectrum Auctions for Multi-party Trading Chapter Summary Further Reading

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) Bidder Participation Fast Auction Clearing Efficient Dynamic Spectrum Auctions

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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-dependent greedy allocation Best known polynomial-time channel allocation schemes are greedy Enable spatial reuse Within a provable distance (Δ: max conflict degree) to the optimal auction efficiency VERITAS-Pricing: Charge every winner i, the bid of its critical neighbor C(i) Critical Neighbor: The neighbor which makes the number of channels available for i drop to 0 Finding Critical Neighbor for i run allocations on {B/bi} (B: set of bids) Ensure truthfulness

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) VERITAS Truthfulness Theorem: VERITAS spectrum auction is truthful, achieves pareto optimal allocations, and runs in polynomial time of O(n 3 k) Proof sketch – Monotone allocations – Monotone allocations: If the bidder wins with bid b, it also wins with b > b when others bids are fixed – Critical value – Critical value: Given a bid-set B, a critical value exists for every allocated bidder – Truthfulness – Truthfulness: If we charge every bidder by its critical value, no bidder has an incentive to lie

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) VERITAS Extensions Support various objective functions VERITAS allocation scheme can sort on broad class of functions of bids The auctioneer can customize based on its needs Bidding Formats Range Format: Every bidder i specifies parameter di, and accepts any number of channels in the range (0, di) Contiguous Format: Bidder requests the channels allocated to be contiguous

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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 Revenue curve not monotonically increasing with # of channels auctioned Effect of the pricing scheme Successful auctions require sufficient level of competition Enforce competition Choose the proper # of channels to auction 13 Choosing the number of channels to be auctioned to maximize revenue

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 19 Outline Rethinking Spectrum Auctions On-demand Spectrum Auctions Economic-Robust Spectrum Auctions Double Spectrum Auctions for Multi-party Trading Chapter Summary Further Reading

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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 Bids Double Auctions: Sellers and buyers are bidders Sellers bid: the minimal price it requires to sell a channel Buyers bid: the maximal price it is willing to pay for a channel Auctioneer as the match maker Select winning buyers and sellers Winners & Prices

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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 $$

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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 Truthfuln ess Individual Rationality Budget Balance Spectrum Reuse McAfees Double Auction X VCG Double Auction XX Extension of Single-sided Truthful Auction X Our Goal

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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

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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 S 1 S 2 … S k-1 S k S k+1 … S m S 1 S 2 … S k-1 S k S k+1 … S m B 1 B 2 … B k-1 B k B k+1 … B n B 1 B 2 … B k-1 B k B k+1 … B n Sellers bidsBuyers bids (k-1) winning buyers, each paying B k (k-1) winning sellers, each getting paid S k Sacrifice one transaction

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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 S 1 S 2 … S k-1 S k S k+1 … S m S 1 S 2 … S k-1 S k S k+1 … S m B 1 B 2 … B k-1 B k B k+1 … B n B 1 B 2 … B k-1 B k B k+1 … B n Buyer Group G1 Buyer Group G2 Buyer Group G3

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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) TRUST: Auction Design Form buyer group Bid- independent Group Formation Decide the bid of each buyer group; Apply McAfee Decide the bid of each buyer group; Apply McAfee Buyer group is 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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Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 27 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

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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 Papers 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. Mualem and N. Nisan, Truthful approximation mechanisms for restricted combinatorial auctions: extended abstract, in Eighteenth national conference on Artificial intelligence, pp. 379–384, 2002. 28

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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.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. 29

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