resource distribution approaches in spectrum sharing systems takefumi yamada 1, dennis burgkhardt 2,...

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, Japan2 Institut fϋr Nachrichtentechnik, Universität Karlsruhe (TH), 76128 Karlsruhe, Germany3DoCoMo Communications Laboratories Europe GmbH, Landsberger Strasse 312, 80687 Munich, Germany

EURASIP Journal on Wireless Communications and Networking (2008)

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OutlineIntroductionCentralized Spectrum Sharing via

Spectrum TradingDecentralized Spectrum Sharing Based on

Game-TheoryExperiment resultsConclusion

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IntroductionRadio 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.

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Introduction－Spectrum Sharing

ApproachesSpectrum 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.

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Centralized Spectrum Sharing via Spectrum Trading－ related workAuctions

◦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.

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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

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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

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Proposed Approach－Several Hierarchic Market LevelsThe highest level has the coarsest time

and spatial allocation◦Done by the regulating bodies◦Time scales encompass years◦Allocation is fixed countrywide

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Proposed Approach－Several Hierarchic Market LevelsThe 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

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TP hr 2

Proposed Approach－Several Hierarchic Market Levels

Day 1

Hour 1

……

Hour 2 ……

Frame 1, 2, 3 ……

TP

TP hr 1

TP day 1

TP TP TP ……

……

……

Time

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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

criticalMcAfee 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.

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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

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First step－ determine resource demand

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Second Step－prepare auction messages(AM)

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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.

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Decentralized Spectrum Sharing Based on Game-TheoryGame-theory provides a mathematical

basis for the analysis of interactive decision-making processes◦3 basic components : players, actions

preferencesAssume 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.

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Decentralized Spectrum Sharing Based on Game-TheoryInequality-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

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Decentralized Spectrum Sharing Based on Game-TheoryBecause the conventional policy is without

considering overall throughput performanceAdjust 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

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Spectrum Sharing Policies－Application

Utility function is used as transmit probability control

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

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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

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Variations in traffic demand

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Result－ an increase efficiency by resource trading

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Result－mean relative outage

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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.

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Result － the more granular the control level is, higher the throughput performance is

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Result ◦ Conventional: collisions

attract collisions◦ Proposed: transmission

probability decreases with offered load increasing

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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

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“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 poolAllows 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.

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ConclusionPropose 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

resource distribution approaches in spectrum sharing systems takefumi yamada 1, dennis burgkhardt 2,...

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