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Fast Spectrum Allocation in Coordinated Dynamic Spectrum Access Based Cellular Networks

Anand Prabhu Subramanian*, Himanshu Gupta*, Samir R. Das* and Milind M. Buddhikot

*Stony Brook University, NY, USABell Labs, Alcatel-Lucent, NJ, USA

anandps@cs.sunysb.edu

Current state-of-the-art in Spectrum Allocation

Static AllocationMulti-year license

agreements

Spectrum is access limited rather than throughput limited

Rigid specification of usage parameters

eg: technology, power,etc

Goal: Break the Spectrum Access Barrier

Enable networks and end user devices to dynamically access variable amount of spectrum on a spatio-temporal scale

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Coordinated Dynamic Spectrum Access (CDSA) Model

Regional Spectrum Broker

Spectrum Demand and Allocation

SpectrumPricing,

Allocation AlgorithmsAnd Policies

Mesh NetworksCellular Networks Fixed Wireless Access

MN

Region R1 MN

Region R2

802.16

CPE

802.16a

CPE

CPE

CPE

Region R4

CPECPE

CPECPE

Region R3

Internet

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Contributions Formulate the Spectrum Allocation

problem in the CDSA model as two optimization problems

Max-Demand DSAMin-Interference DSA

Design fast and efficient algorithms with provable performance guarantees

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Spectrum Allocation – Reference Architecture

Spectrum Broker

A region R controlled by the Spectrum Broker

Base stations of different RIPsC

oo

rdin

ated

Acc

ess

Ban

d

Demands:(dmin , dmax)

BatchedDemand

ProcessingModel

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Interference Constraints

21

3 54

8 109

7 617 1918

14 1615

11 12 13

23 2524

272620

21 22Different RIPs

Co-located Cross Provider Constraint

Remote Cross ProviderConstraint

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Interference Constraints

21

3 54

8 109

7 617 1918

14 1615

11 12 13

23 2524

272620

21 22Different RIPs

Soft Hand-off Constraint

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Interference Graph

1 2

3

4

5

6

7

8

9

1211

10

1

Base stations of different RIPs

2

3

7

6

45

8

9

11

10

12

Spectrum Allocation Variation of Graph Coloring

Cannot always find a feasible solution Formulate as optimization problems

Max-Demand DSA Min-Interference DSA

NP Hard

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Max-Demand DSA

Maximize the overall demands served among all base stations with the available number of channels such that no constraint is violated

Input to the problem: Interference Graph Minimum and maximum demands of each node Available number of channels

Check if the minimum demands of all base stations can be servedIf yes, serve as many demands as possible using available channels

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Max-Demand DSA Algorithm

1 2

43

G(V,E)

dmin=211 22

33 44

Gmin(Vmin,Emin)

Pick K independent sets (IS) in Gmin If all nodes in Gmin are picked proceed to Phase II Phase II: Add dmax(i)-dmin(i) copies for each node i to construct Gmax Pick as many independent sets as possible in Gmax

Phase I:

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Max-Demand DSA Algorithm: Performance GuaranteeInterference Graph is modeled as a δ-degree

bounded graphWhen picking independent sets, pick the nodes

in the order of maximum degree.We can prove that

|IS||OPT|

δ Phase II of the Max-Demand DSA achieves an approximation ratio of 1- 1

e1δ

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Min-Interference DSA

Input to the problem: Interference Graph Maximum demands of each node Available number of channels

Minimize overall Interference when all demand (dmax) of the base stations are serviced

1 2

3

4

5

6

7

8

9

1211

10

Max K Cut:

Assign nodes todifferent colors so as

maximize the number ofedges between nodes with different colors

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Algorithm Rk for Multi-Color Max-K-Cut:

For each node i, randomly pick dmax(i) different colors from the available K colors

1 2

3

4

5

6

7

8

9

1211

10

dmax=2 K=5

21

1 2

3 3

4

45 5

6

6

77

8 8

9

9

10 10

11

12 12

11

By a simple probability argument, we can prove that the weight of the cut (edges crossing partitions) produced by RK is1-1/K of the optimal

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Min-Interference DSA: TABU Search Algorithm

Start from the random solutionIn each iteration, generate certain number of

neighboring solutionsPick the solution with least interferenceRepeat until no improvement for certain

number of iterations

21 1 23 3 445 5 66 778 8

9

9 10 1011

12 1211

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Performance

Graph Based simulations with 1000 nodes40 - 240 channelsDemands 10 - 80

Max-Demand DSA performs very well

Min-Interference DSA: Random 1/KMin-Interference DSA: Tabu performs

extremely well compared to Random

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Future Work

Test our algorithm performance on realistic network topologies from existing service providers

Build an experimental spectrum broker simulator that accounts for advanced features of the CDSA model such as demand scope, stickiness, fairness etc.