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Resource Distribution Approaches in Spectrum Sharing Systems

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

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Resource Distribution Approaches in Spectrum Sharing Systems

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  1. Resource Distribution Approaches in Spectrum Sharing Systems Takefumi Yamada1, Dennis Burgkhardt2, Ivan Cosovic3, and Friedrich K. Jondral2 1NTT DoCoMo, Inc., 3-5 Hikari-no-oka, Yokosuka-shi, Kanagawa 239-8536, Japan 2 Institut fϋr Nachrichtentechnik, Universität Karlsruhe (TH), 76128 Karlsruhe, Germany 3DoCoMo Communications Laboratories Europe GmbH, LandsbergerStrasse 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

  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.

  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 [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] [11] C. Kl¨ock, H. Jaekel, and F. Jondral, “Auction sequence as a new resource allocation mechanism,” in Proceedings of the 61st 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

  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

  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

  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

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

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

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

  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 • Pt: transaction price [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] [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 • xi:payoff for the ith operator • n: number of operators sharing the spectrum • Ai: priority level of ith operator for payoff • αi: reacting factor against higher payoff operators • βi: reacting factor against lower payoff operators

  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 • Ci: adjusting coefficient for utility function • Call: total amount of shared spectrum • Cblank,i: unused spectrum measures by ith operator • Ccoll,i: spectrum loss caused by signal collision • γ: sensitivity for the spectrum loss over the unused spectrum

  19. SpectrumSharingPolicies－ Application • Utility function is usedastransmitprobabilitycontrol • Apply the proposed policy, the transmit probability pi(t) is given by • ∆Pi(t): update to transmit probability of the ith operator at time t

  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

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

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

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

  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 • Ti : throughput for the ith operator • Ai : weight for the ith operator (according traffic demand) [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 －themoregranularthecontrollevelis,higherthethroughputperformanceis

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

  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

  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 [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

  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

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