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High Speed Networks Budapest University of Technology and Economics High Speed Networks Laboratory General Distributed.

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1 High Speed Networks Budapest University of Technology and Economics High Speed Networks Laboratory General Distributed Economic Framework for Dynamic Spectrum Allocation Attila VIDÁCS, László TOKA, László Kovács HSN Lab, Dept. of Telecommunications and Media Informatics Budapest University of Technology and Economics (BME-TMIT) 1| June, 2009 | FuturICT 2009, Budapest, Hungary

2 High Speed Networks Laboratory Motivation Goal Scalable and distributed framework for DSA Emphasis also on the economic perspective Modeling approach Spatio-temporal DSA scheme Game Theoretic modeling Mechanism design Proposed allocation and pricing scheme Outline | June, 2009 | FuturICT 2009, Budapest, Hungary2

3 High Speed Networks Laboratory Actual radio spectrum allocation is not efficient due to rigid regulation: access-limited (i.e., big player syndrome) peak traffic planning causes temporal underutilization since spectrum demands vary in time spatial and spectral restrictions on frequency re-usage Service convergence Enabling technologies: New generation radio interfaces support flexible transmission frequencies e.g., cognitive radio, Long Term Evolution, etc. Motivation | June, 2009 | FuturICT 2009, Budapest, Hungary3

4 High Speed Networks Laboratory Spectrum demands vary in time and space spatio-temporal Dynamic Spectrum Allocation (DSA) Scalable and distributed economic framework for DSA to allocate the frequency bands for wireless service providers with the goal of improving the efficiency of spectrum utilization; in a self-organizing scheme in which the participants manage the allocation and pricing in a distributed way; the central authority is present only for control purposes; where interference is modeled in a general way; to charge for the usage (pricing). Methodology: Mechanism design to assure desirable properties Goal | June, 2009 | FuturICT 2009, Budapest, Hungary4

5 High Speed Networks Laboratory Our basic principles: Overall spectrum utilization should be maximal In the case of conflict of interest the frequency bands are allocated to those who value it most Participants: frequency owner and nodes (users) The model takes into account the selfishness of the participants (in a Game-theoretic way) Nodes (players): frequency leasers that exploit radio bands within delimitable geographic zones (e.g., base stations) Temporal-bounded license of frequency band units Participants are modeled by their valuation towards spectrum i.e., willingness to pay for license Bidding: participants make bids to acquire necessary licenses to provide their service Game-theoretic modeling | June, 2009 | FuturICT 2009, Budapest, Hungary5

6 High Speed Networks Laboratory Dividing spectrum is not the same as dividing other goods! (mainly because of interference and tolerance!) A birthday cake analogy: People at a birthday party sitting next to each other can have neighbouring slices of the cake. Guest dont poke into each others plate. They get (more or less) the same amount. They use it for the same purpose. The 12-slice cake have exactly 12 slices after cut into pieces. The cake is cut only along one dimension (vertically). The first slice tastes the same as the last one. Allocating spectrum… | June, 2009 | FuturICT 2009, Budapest, Hungary6

7 High Speed Networks Laboratory General, physical model (point-to-point) more realistic than modeling by conflict graph significant complexity of allocation Measured SINR as inter-node effects interference noise geographic coupling service-type coupling (power, coding, etc.) Central authority controls and enforces transmitting power levels of nodes 7| June, 2009 | FuturICT 2009, Budapest, Hungary Interference model

8 High Speed Networks Laboratory Users utility is based on discount estimated income from its service Users place bids for required frequency bands Bidding against the actual license holder *IF* inter-node interference overgrows bearable limit If multiple bidders for the same frequency band, a second- price (or Vickrey) auction is carried out i.e., the highest bidder wins and pays the second bid User buy-outs may happen when a new user successfully overbids actual leaser that causes inter-node jamming Pricing: payment division at spectrum re-selling (discounted cost) on second price Allocation and Pricing 8| June, 2009 | FuturICT 2009, Budapest, Hungary

9 High Speed Networks Laboratory Strategy: To buy out the cheapest interfering player set possible to assure own service quality Iterative spectrum allocation algorithm: Define interference matrix that describes inter-node effects; Define node valuations and required frequency bands; Every participant runs heuristic optimization to minimize cost – buys the cheapest band, conform to demand, with the cheapest necessary buy-outs. Distributed Algorithm 9| June, 2009 | FuturICT 2009, Budapest, Hungary

10 High Speed Networks Laboratory Distributed Algorithm 10| June, 2009 | FuturICT 2009, Budapest, Hungary

11 High Speed Networks Laboratory Incentive compatibility (truthfulness) Players report their true presentation valuations when bidding for spectrum in DDSA. Key: Vickrey-auctions, cost division Fairness and efficiency Less interference-friendly nodes pay relatively more for the spectrum. Only high valuation enables a node to eliminate interference. Nodes that cause heavy interference must have high valuation Key: iterative one-way exclusion 11| June, 2009 | FuturICT 2009, Budapest, Hungary Consequences

12 High Speed Networks Laboratory 12| June, 2009 | FuturICT 2009, Budapest, Hungary Advantages to central DSA Temporal flexibility. No central intelligence needed. Scalable: distributed optimization. Same outcome (at least for a simple simulation). Summary: We proposed a general dynamic DSA framework that offers a distributed mechanism design, well suited to practical employment issues. The model handles interference effects without any restricting assumptions. The solution provides scalable and incentive-compatible allocation and pricing mechanisms. Evaluation

13 High Speed Networks Laboratory | June, 2009 | FuturICT 2009, Budapest, Hungary13 Thank you for your attention


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