Spectrum Sharing for Unlicensed Bands Raul Etkin, Abhay Parekh, and David Tse Dept of EECS U.C....

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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

Transcript of Spectrum Sharing for Unlicensed Bands Raul Etkin, Abhay Parekh, and David Tse Dept of EECS U.C....

Page 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.

Spectrum Sharing for Unlicensed Bands

Spectrum Sharing for Unlicensed Bands

Raul Etkin, Abhay Parekh, and David TseDept of EECSU.C. Berkeley

Project supported by NSF ITR ANI-0326503 grant

DySPAN 2005, Nov. 10, 2005

Page 2: 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.

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Problem: Spectrum SharingCan multiple heterogeneous wireless systems coexist and

share spectrum in a fair and efficient manner?

•Unlicensed setting

•Equal rights

•Different goals

Introduction

Page 3: 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.

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Main Goals

• Find spectrum sharing rules that are:– Efficient– Fair– Robust against selfish behavior

• Study how to obtain good performance without

cooperation.

Introduction

Page 4: 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.

4Spectrum Sharing for Unlicensed Bands

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.

Introduction

C1,1

C2,2

C1,2

C2,1

N0

N0

noise interference

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Static Gaussian Interference Game

• M Players: the M systems

• Strategy of system: power allocation satisfying power

constraint

• Utility of system i non-decreasing, concave on Ri.

• All parameters ({ci,j},{Pi},N0) are common knowledge.

• Players select their actions simultaneously.

Non-cooperative Scenarios

Page 6: 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.

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Static Game Analysis

Non-cooperative Scenarios

full spread Nash equilibrium

Achievable rates

proportional fair

orthogonal

Unique if

XXinterference

limited

noise limited

price of anarchy

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Dynamic Game• What rate vectors are achievable as a N.E. in the dynamic game ?

Non-cooperative Scenarios

achievable with self enforcing strategies

Punishment strategies: encourage cooperation by threatening to spread

good behavior

punishment

Page 8: 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.

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Example A

Non-cooperative Scenarios

asymmetry in power and gains

802.11 bluetooth

full spread N.E.

proportional fair

Page 9: 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.

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Example B

Non-cooperative Scenarios

asymmetry in power

802.11

bluetooth

full spread N.E.

proportional fairQ: Can be achieved with other self enforcing strategies ?

No !

best PF self enforcing point

Page 10: 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.

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Asymmetry and Fairness

Non-cooperative Scenarios

No Loss

No Loss

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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.

Conclusions

Page 12: 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.

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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]

Introduction