Title: Fast Spectrum Allocation in Coordinated Dynamic Spectrum Access Based Cellular Networks
1Fast 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
2Current 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
3Coordinated Dynamic Spectrum Access (CDSA) Model
4Contributions
- 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
5Spectrum Allocation Reference Architecture
A region R controlled by the Spectrum Broker
Batched Demand Processing Model
Base stations of different RIPs
6Interference Constraints
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Remote Cross Provider Constraint
Co-located Cross Provider Constraint
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Different RIPs
7Interference Constraints
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Different RIPs
8Interference Graph
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Spectrum Allocation
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Variation of Graph Coloring
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- Cannot always find a feasible solution
- Formulate as optimization problems
- Max-Demand DSA
- Min-Interference DSA
- NP Hard
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9Max-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
10Max-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
11Max-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
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- Phase II of the Max-Demand DSA
- achieves an approximation ratio of
12Min-Interference DSA
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Max K Cut Assign nodes to different colors so
as maximize the number of edges between nodes
with different colors
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Minimize overall Interference when all demand
(dmax) of the base stations are serviced
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13Algorithm Rk for Multi-Color Max-K-Cut
For each node i, randomly pick dmax(i) different
colors from the available K colors
14Min-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
15Performance
- 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
16Future 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.