1. Resource Distribution Approaches in Spectrum Sharing Systems Takefumi Yamada 1, Dennis Burgkhardt 2, Ivan Cosovic 3, and Friedrich K. Jondral 2 1 NTT DoCoMo, Inc., 3-5 Hikari-no-oka, Yokosuka-shi, Kanagawa 239-8536, Japan 2 Institut fϋr Nachrichtentechnik, Universität Karlsruhe (TH), 76128 Karlsruhe, Germany 3 DoCoMo Communications Laboratories Europe GmbH, Landsberger Strasse 312, 80687 Munich, Germany EURASIP Journal on Wireless Communications and Networking (2008)

2. Outline Introduction Centralized Spectrum Sharing via Spectrum Trading Decentralized Spectrum Sharing Based on Game-Theory Experiment results Conclusion 2

3. Introduction Radio Spectrum assignment and coordination has been under government administration. ◦ Licensing is to avoid interference and collisions. ◦ Reduce the risk of spectrum acquisition. Market demand is increasing and there is insufficient spectrum to use. ◦ USA FCC, Europe, and Japan. 3

4. Introduction － Spectrum Sharing Approaches Spectrum Access Priority ◦ Vertical sharing(VS)  Spectrum pooling approach [13] ◦ Horizontal sharing(HS)  Wireless local area networks(WLAN) Architecture Assumption ◦ Centralized ◦ Decentralized  CSMA/CA protocols and game-theory 4 [13] T. A. Weiss and F. K. Jondral, “Spectrum pooling: an innovative strategy for the enhancement of spectrumefficiency,” IEEE Communications Magazine, vol. 42, no. 3, pp. S8–14, 2004.

5. Centralized Spectrum Sharing via Spectrum Trading － related work Auctions ◦ Allocation of UMTS frequency bands [21] ◦ Real-time auctions between service providers and users [11] ◦ Auctions on the interoperator level [12] 5 [11] C. Kl¨ock, H. Jaekel, and F. Jondral, “Auction sequence as a new resource allocation mechanism,” in Proceedings of the 61 st IEEE Vehicular Technology Conference (VTC ’05), vol. 1, pp. 240–244, Stockholm, Sweden, September 2005. [12] D. Grandblaise, K. Moessner, G. Vivier, and R. Tafazolli, “Credit token based rental protocol for dynamic channel allocation,” in Proceedings of the 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom ’06), pp. 1–5, Mykonos Island, Greece, June 2006. [21] P. Jehiel and B. Moldovanu, “The European UMTS/IMT-2000 license auctions,” Sonderforschungsbereich 504 Publications, Sonderforschungsbereich 504, Universit¨at Mannheim & Sonderforschungsbereich 504, University of Mannheim, Mannheim, Germany, 2001.

6. Concept of Hierarchic Trading Two-level hierarchy trading approach ◦ Start from a given spectrum allocation ◦ By trading, this initial allocation is adapted on cell level and valid in short-term time frame ◦ A new trading period will determine another adapted cell-specific allocation, from original state 6

7. Concept of Hierarchic Trading Short-term basis advantages: ◦ Improve the efficiency of spectrum use  Estimation of required resources can be accurate  Depend on traffic load to trade resources Long-term basis advantages: ◦ The available frequency channels are reliable ◦ Avoid inter-cell interference 7

8. Proposed Approach － Several Hierarchic Market Levels The highest level has the coarsest time and spatial allocation ◦ Done by the regulating bodies ◦ Time scales encompass years ◦ Allocation is fixed countrywide 8

9. Proposed Approach － Several Hierarchic Market Levels The lowest level is composed of the elementary short-term frames ◦ An hour can be the time unit in the lowest level ◦ One common cell represents an elementary market place 9

10. TP hr 2 Proposed Approach － Several Hierarchic Market Levels 10 Day 1 Hour 1 …… Hour 2 …… Frame 1, 2, 3 …… TP TP hr 1 TP day 1 TP …… Time

11. Proposed Approach － Double Auction Scheme Double auction: buyers and sellers simultaneously submit their prices to an auctioneer Discontinuous double auction ◦ Operator sends his bid once for each trading frame ◦ The order in which bids and asks arrive is not critical McAfee double auction protocol [22] 11 [22] R. P.McAfee, “A dominant strategy double auction,” Journal of Economic Theory, vol. 56, no. 2, pp. 434–450, 1992.

12. Proposed Approach － Trading Mechanism In each market level and area a dedicated logical broker is in operation ◦ The brokers are software agents ◦ Outcome of an auction will be passed down to the lower level 12

13. Proposed Approach － Trading Mechanism First step － determine resource demand 13

14. Proposed Approach － Trading Mechanism Second Step － prepare auction messages(AM) 14

15. Proposed Approach － Trading Mechanism The broker using McAfee double auction protocol [22] to determine the transactions Using transaction message(TM) to inform operators of their trading (s|b|0): bought, sold or no transaction N: number of resources traded P t : transaction price 15 [22] R. P.McAfee, “A dominant strategy double auction,” Journal of Economic Theory, vol. 56, no. 2, pp. 434–450, 1992.

16. Decentralized Spectrum Sharing Based on Game-Theory Game-theory provides a mathematical basis for the analysis of interactive decision-making processes ◦ 3 basic components : players, actions preferences Assume all operators desire a sustainable wireless communication environment ◦ Inequality-aversion model [15] 16 [15] H. Gintis, Game Theory Evolving: A Problem-Centered Introduction to Modeling Strategic Interaction, Princeton University Press, Princeton, NJ, USA, 2000.

17. Decentralized Spectrum Sharing Based on Game-Theory Inequality-aversion utility function x i : payoff for the ith operator n: number of operators sharing the spectrum A i : priority level of ith operator for payoff α i : reacting factor against higher payoff operators β i : reacting factor against lower payoff operators 17

18. Decentralized Spectrum Sharing Based on Game-Theory Because the conventional policy is without considering overall throughput performance Adjust the utility functions with spectrum usage status C i : adjusting coefficient for utility function C all : total amount of shared spectrum C blank,i : unused spectrum measures by ith operator C coll,i : spectrum loss caused by signal collision γ: sensitivity for the spectrum loss over the unused spectrum 18

19. Spectrum Sharing Policies － Application Utility function is used as transmit probability control Apply the proposed policy, the transmit probability pi(t) is given by ∆P i (t): update to transmit probability of the ith operator at time t 19

20. Experiment results － Centralized Approaches via Spectrum Trading Simulation configuration ◦ trading in one cell ◦ In the cell, 100 channels are available ◦ “Level 0” (L0) is the lowest level (most granular) ◦ “Level 2” (L2) is the highest level (coarsest) ◦ 8 operators compete for resources ◦ 1 L1 period is composed of 40 L0 trades ◦ “Random walk” to model traffic variations on L0 20

21. Experiment results － Centralized Approaches via Spectrum Trading Variations in traffic demand 21

22. Experiment results － Centralized Approaches via Spectrum Trading Result － an increase efficiency by resource trading 22

23. Experiment results － Centralized Approaches via Spectrum Trading Result － mean relative outage 23

24. Experiment results － Decentralized Approaches Based on Game-Theory Assumptions for resource channels, operators, trading levels, and traffic model are the same as centralized model Using fairness index (FI) [25] to evaluate policy ◦ T i : throughput for the ith operator ◦ A i : weight for the ith operator (according traffic demand) 24 [25] R. Jain, G. Babic, B. Nagendra, and C. Lam, “Fairness, call establishment latency and other performance mertics,” Tech. Rep. ATM Forum/96-1173, ATM Forum, Columbus, Ohio, USA, August 1996.

25. Experiment results － Decentralized Approaches Based on Game-Theory Result － the more granular the control level is, higher the throughput performance is 25

26. Experiment results － Decentralized Approaches Based on Game-Theory Result ◦ Conventional: collisions attract collisions ◦ Proposed: transmission probability decreases with offered load increasing 26

27. Experiment results － Hybrid Approach for Centralized and Decentralized Sharing Overall throughput performance: (demand=100) ◦ Centralized: 0.95 (L0) ◦ Decentralized: 0.41 Cost: ◦ Centralized: negotiation cost for brokerage ◦ Decentralized: unused spectra or collisions In order to flexibly control the tradeoff, a hybrid method is proposed 27

28. Experiment results － Hybrid Approach for Centralized and Decentralized Sharing “Spectrum Pooling” concept [26] Proposed hybrid approach: ◦ L1 level and higher use the centralized trading mechanism ◦ Put the estimated unused channels into the pool and broadcast ◦ Only operators who need more channels join the spectrum sharing game over the pool Allows a flexible tradeoff between spectrum loss and central negotiation cost 28 [26] J. Mitola III, “Cognitive radio for flexible mobile multimedia communications,” in Proceedings of the IEEE International workshop on Mobile Multimedia Communications (MoMuC ’99), pp. 3–10, San Diego, Calif, USA, November 1999.

29. Experiment results － Hybrid Approach for Centralized and Decentralized Sharing 29

30. Conclusion Propose a spectrum trading mechanism in a centralized manner, and a policy for decentralized spectrum sharing The tradeoff between the two approaches is important to consider The hybrid approach balances the two costs 30