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Spectrum Sharing for Unlicensed Bands

Spectrum Sharing for Unlicensed Bands . Raul Etkin, Abhay Parekh, and David Tse Dept of EECS U.C. Berkeley Project supported by NSF ITR ANI-0326503 grant DySPAN 2005, Nov. 10, 2005. Introduction. Problem: Spectrum Sharing.

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Spectrum Sharing for Unlicensed Bands

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  1. Spectrum Sharing for Unlicensed Bands Raul Etkin, Abhay Parekh, and David Tse Dept of EECS U.C. Berkeley Project supported by NSF ITR ANI-0326503 grant DySPAN 2005, Nov. 10, 2005

  2. Introduction Problem: Spectrum Sharing Can multiple heterogeneous wireless systems coexist and share spectrum in a fair and efficient manner? • Unlicensed setting • Equal rights • Different goals

  3. Introduction Main Goals • Find spectrum sharing rules that are: • Efficient • Fair • Robust against selfish behavior • Study how to obtain good performance without cooperation.

  4. Introduction The Model • Flat Fading • Systems use Gaussian signals with PSD {pi(f)}. • Power constraint for each system. • Total bandwidth W. • Interference treated as noise. • Design choice: power allocations over frequency. N0 C1,1 C1,2 C2,1 N0 C2,2 noise interference

  5. Non-cooperative Scenarios Static Gaussian Interference Game • M Players: the M systems • Strategy of system: power allocation satisfying power constraint • Utility of system i non-decreasing, concaveon Ri. • All parameters ({ci,j},{Pi},N0) are common knowledge. • Players select their actions simultaneously.

  6. Non-cooperative Scenarios Static Game Analysis orthogonal noise limited Achievable rates price of anarchy proportional fair X interference limited X full spread Nash equilibrium Unique if

  7. Non-cooperative Scenarios Dynamic Game • What rate vectors are achievable as a N.E. in the dynamic game ? good behavior punishment achievable with self enforcing strategies Punishment strategies: encourage cooperation by threatening to spread

  8. 802.11 bluetooth Non-cooperative Scenarios Example A asymmetry in power and gains proportional fair full spread N.E.

  9. 802.11 bluetooth Non-cooperative Scenarios Example B asymmetry in power proportional fair Q: Can be achieved with other self enforcing strategies ? best PF self enforcing point No ! full spread N.E.

  10. Non-cooperative Scenarios Asymmetry and Fairness No Loss No Loss

  11. Conclusions Conclusions • With complete information and moderate asymmetry it is possible to find policies that are fair, efficient and robust against selfish behavior. • Results can be extended to: • Non-Gaussian signals • Any achievable rate region (with interference cancellation, etc.) • Future research: • Find distributed algorithms that do not require complete information and approximate the performance predicted here. • Investigate how to deal with cases of extreme asymmetry.

  12. Introduction Related Work • Distributed optimization of power spectral allocations for DSL using iterative waterfilling [Cioffi, et al. 2001] • Use of Game Theory to analyze outcomes of iterative waterfilling algorithm [Cioffi, et al., 2002] • Iterative waterfilling may lead to poor performance. Signal space partitioning often leads to better results. [Popescu, Rose & Popescu, 2004] • Use of genetic algorithms to find good strategies in repeated games with small strategy space. [Clemens & Rose, DySPAN 05]

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