Chapter 17

1. Chapter 17 Auction-based spectrum markets in cognitive radio networks

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

3. Recent Spectrum Auction Activities $19 billion 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)

4. 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 Sellers Market-based Spectrum Trading Buyers

5. 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 1 6 2 3 5 4

6. 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 1 6 2 3 5 4

7. 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 Large # of bidders Can we design low-complexity and yet efficient auction solutions for large scale systems? Real-time auctions

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

9. System Overview User Auctioneer Bidding Pricing Model Allocation (clearing) Piecewise Linear Price Demand bids– a compact and yet highly expressive bidding format Uniform vs. Discriminatory pricing models – tradeoffs between efficiency and fairness Fast auction clearing algorithms for both pricing models 1 6 5 4 How to set prices? how to handle the bids to efficiently maximize revenue? 3 How do users bid? 2

10. 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

11. Analytical Bounds CAUP Clearing Algorithm for Uniform Pricing CADP Clearing Algorithm for Discriminatory Pricing Theoretical bounds Revenue efficiency Complexity When the conflict graph is a tree

12. 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

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

14. New Challenge: Eliminate Fear of Market Manipulation Solution: Make the auction truthful; bid by your true value, regardless of how others bid

15. VERITAS: Truthful and Efficient Spectrum Auctions • VERITAS-Allocation: • Bid-dependent greedyallocation • 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

16. VERITAS Truthfulness • Theorem: VERITAS spectrum auction is truthful, achieves pareto optimal allocations, and runs in polynomial time of O(n3k) • Proof sketch • Monotone allocations: If the bidder wins with bid b, it also wins with b’ > b when others’ bids are fixed • Critical value: Given a bid-set B, a critical value exists for every allocated bidder • Truthfulness: If we charge every bidder by its critical value, no bidder has an incentive to lie

17. 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

18. 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 Choosing the number of channels to be auctioned to maximize revenue 13

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

20. Enabling Trading by Double Auctions Winners & Prices • 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 Bids Bids Sellers Buyers

21. Need Judicious Auction Designs • 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 $ $ Bids Bids Sellers Buyers

22. Existing Solutions No Longer Apply

23. 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

24. McAfee Double Auctions Buyers’ bids Sellers’ bids • Achieve budget balance, truthfulness, individual rationality without spectrum reuse (k-1) winning buyers, each paying Bk B1 B2 … Bk-1 Bk Bk+1 … Bn S1 S2 … Sk-1 Sk Sk+1 … Sm (k-1) winning sellers, each getting paid Sk ≥ ≥ Sacrifice one transaction ≥ ≥ ≤

25. Enabling Spectrum Reuse Buyers’ bids Sellers’ bids • Map a group of non-conflicting buyers to one seller Buyer Group G1 B1 B2 … Bk-1 Bk Bk+1 … Bn S1 S2 … Sk-1 Sk Sk+1 … Sm ≥ Buyer Group G3 ≥ ≥ ≥ Buyer Group G2 ≤

26. TRUST: Auction Design Form buyer group Decide the bid of each buyer group; Apply McAfee Charge individuals in a winning buyer group Bid-independent Group Formation Buyer group i’s bid = The lowest bid in group i * #of bidders in group i Uniform pricing within one winning buyer group Theorem 1. TRUST is ex-post budget balanced, individual rational, and truthful.

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

28. 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. 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.

29. 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.