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

Transcription

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

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

3 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)

4 Addressing Inefficient Spectrum Distribution Legacy wireless providers own the majority of spectrum But cannot fully utilize it Sellers Market based Spectrum Trading New wireless providers are dying for usable spectrum But have to crowd into limited unlicensed bands Buyers

5 Rethinking Spectrum Auctions Dynamic 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

6 Why Auctions? Dynamic Spectrum 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

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 Can we design low complexity and yet efficient auction solutions for large scale systems? NP-hard resource allocation problem Large # of bidders 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 8

9 System Overview User Bidding Pricing Model Auctioneer 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 How to set prices? how to handle the bids to efficiently maximize revenue? How do users bid?

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 Theoretical bounds CAUP Clearing Algorithm for Uniform Pricing CADP Clearing Algorithm for Discriminatory Pricing Revenue efficiency R 3 1 CAUP R OPT UP R n 3( n 1) CADP R OPT DP Complexity O( nlog n nlogu ) polynomial When the conflict graph is a tree R CAUP R OPT UP R CADP R OPT DP

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 13

14 Bidder Participation Fast Auction Clearing Efficient Dynamic Spectrum Auctions

15 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

16 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: 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 19

20 Enabling Trading by Double Auctions Double Auctions: Winners & Prices 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 Bids Bids Auctioneer as the match maker Select winning buyers and sellers 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 McAfee s Double Auction Truthfuln ess Individual Rationality Budget Balance Spectrum Reuse X VCG Double Auction X X Extension of Single-sided Truthful Auction X Our Goal

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 (k-1) winning buyers, each paying B k Buyers bids B 1 B 2 B k-1 Sellers bids S 1 S 2 S k-1 (k-1) winning sellers, each getting paid S k B k B k+1 B n S k S k+1 S m Sacrifice one transaction Achieve budget balance, truthfulness, individual rationality without spectrum reuse

25 Enabling Spectrum Reuse Buyer Group G1 Buyer Group G2 Buyers bids Sellers bids B 1 B 2 B k-1 Buyer Group G3 B k B k+1 S 1 S 2 S k-1 S k S k+1 B n S m Map a group of non-conflicting buyers to one seller

26 TRUST: Auction Design Form buyer group Decide the bid of each buyer group; Apply McAfee Charge individuals in a winning buyer group Bidindependent 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 27

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, X. Zhou, S. Gandhi, S. Suri, and H. Zheng, ebay in the sky: Strategy-proof wireless spectrum auctions, in Proc. of MobiCom, Sept X. Zhou and H. Zheng, TRUST: A general framework for truthful double spectrum auctions, in Proc. of INFOCOM, April Further readings: S. Olafsson, B. Glower, and M. Nekovee, Future management of spectrum, BT Technology Journal, vol. 25, no. 2, pp , Ofcom, Spectrum framework review, June M. Buddhikot et. al., Dimsumnet: New directions in wireless networking using coordinated dynamic spectrum access, in Proc. of IEEE WoWmoM05, June 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 W. Vickery, Counterspeculation, auctions and competitive sealed tenders, Journal of Finance, vol. 16, pp. 8 37, D. Lehmann, L. O callaghan, and Y. Shoham, Truth revelation in approximately efficient combinatorial auctions, J. ACM, vol. 49, no. 5, pp , A. Mu alem and N. Nisan, Truthful approximation mechanisms for restricted combinatorial auctions: extended abstract, in Eighteenth national conference on Artificial intelligence, pp ,

29 References & Further Readings R. P. McAfee, A dominant strategy double auction, Journal of Economic Theory, vol. 56, pp , April 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 Spectrum Bridge Inc., 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, 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 , 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 , V. Rodriguez, K. Mossner, and R. Tafazoli, Auction-based optimal bidding, pricing and service priorities for multirate, multi-class CDMA, in Proc. Of IEEE PIMRIC, pp , September J. Huang, R. Berry, and M. L. Honig, Auction-based spectrum sharing, ACM Mobile Networks and Applications, vol. 11, no. 3, pp ,