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Fast Spectrum Allocation in Coordinated Dynamic Spectrum Access Based Cellular Networks

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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 – PowerPoint PPT presentation

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Title: Fast Spectrum Allocation in Coordinated Dynamic Spectrum Access Based Cellular Networks


1
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, USA Bell Labs,
Alcatel-Lucent, NJ, USA
2
Current state-of-the-art in Spectrum Allocation
Multi-year license agreements
Static Allocation
  • Goal
  • Break the Spectrum Access Barrier
  • Enable networks and end user devices to
    dynamically access variable amount of spectrum
    on a spatio-temporal scale

Spectrum is access limited rather than
throughput limited
3
Coordinated Dynamic Spectrum Access (CDSA) Model
4
Contributions
  • Formulate the Spectrum Allocation problem in the
    CDSA model as two optimization problems
  • Max-Demand DSA
  • Min-Interference DSA
  • Design fast and efficient algorithms with
    provable performance guarantees

5
Spectrum Allocation Reference Architecture
A region R controlled by the Spectrum Broker
Batched Demand Processing Model
Base stations of different RIPs
6
Interference Constraints
11
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Remote Cross Provider Constraint
Co-located Cross Provider Constraint
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Different RIPs
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Interference Constraints
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Different RIPs
8
Interference Graph
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1
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Spectrum Allocation
3
Variation of Graph Coloring
6
  • 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
  • Check if the minimum demands of all base stations
    can be served
  • If yes, serve as many demands as possible using
    available channels

10
Max-Demand DSA Algorithm
Phase I
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G(V,E)
Gmin(Vmin,Emin)
  • Pick K independent sets (IS) in Gmin
  • If all nodes in Gmin are picked proceed to Phase
    II

11
Max-Demand DSA Algorithm Performance Guarantee
  • Interference Graph is modeled as a d-degree
    bounded graph
  • When picking independent sets, pick the nodes in
    the order of maximum degree.
  • We can prove that
  • Phase II of the Max-Demand DSA
  • achieves an approximation ratio of

12
Min-Interference DSA
2
1
Max K Cut Assign nodes to different colors so
as maximize the number of edges between nodes
with different colors
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3
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Minimize overall Interference when all demand
(dmax) of the base stations are serviced
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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
14
Min-Interference DSA TABU Search Algorithm
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  • Start from the random solution
  • In each iteration, generate certain number of
    neighboring solutions
  • Pick the solution with least interference
  • Repeat until no improvement for certain number of
    iterations

15
Performance
  • Graph Based simulations with 1000 nodes
  • 40 - 240 channels
  • Demands 10 - 80
  • Max-Demand DSA performs very well
  • Min-Interference DSA Random ? 1/K
  • Min-Interference DSA Tabu performs extremely
    well compared to Random

16
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.
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