high speed networks laboratory @ budapest university of technology and economics high speed...

High Speed Networks Laboratory@ Budapest University of Technology and Economics

http://hsnlab.tmit.bme.hu

High Speed Networks Laboratory

General Distributed Economic Framework for Dynamic Spectrum AllocationAttila VIDÁCS, László TOKA, László Kovács

HSN Lab, Dept. of Telecommunications and Media InformaticsBudapest University of Technology and Economics (BME-TMIT)

1 | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

High Speed Networks Laboratoryhttp://hsnlab.tmit.bme.hu

• 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

| 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary2


• 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



• 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



• 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



• 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 don’t poke into each other’s 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…



• 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


Interference model

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• User’s 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



• 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



Distributed Algorithm



• Incentive compatibility („truthfulness”)Players report their true presentation valuations when bidding for spectrum in DDSA.• Key: Vickrey-auctions, cost division

• Fairness and efficiencyLess 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


Consequences



• 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



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Thank you for your attention

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