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![Page 1: Anandps@cs.sunysb.edu Fast Spectrum Allocation in Coordinated Dynamic Spectrum Access Based Cellular Networks Anand Prabhu Subramanian*, Himanshu Gupta*,](https://reader036.fdocuments.us/reader036/viewer/2022062717/56649e1b5503460f94b08ef6/html5/thumbnails/1.jpg)
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
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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
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7
8
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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.