General Distributed Economic Framework for Dynamic Spectrum Allocation

1. General Distributed Economic Framework for Dynamic Spectrum Allocation Attila VIDÁCS, László TOKA, László KovácsHSN Lab, Dept. of Telecommunications and Media Informatics Budapest University of Technology and Economics (BME-TMIT) | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

2. Outline • 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 | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

3. Motivation • 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. | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

4. Goal • Spectrumdemands 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 | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

5. Game-theoretic modeling • 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 | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

6. Allocating spectrum… • 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. | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

7. Interference model • 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 | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

8. Allocation and Pricing • 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 | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

9. Distributed Algorithm • 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. | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

10. Distributed Algorithm | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

11. Consequences • 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 | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

12. Evaluation • 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. | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary

13. http://www.hsnlab.hu Thank you for your attention | 29-30 June, 2009 | FuturICT 2009, Budapest, Hungary