in NonContiguous DSA with Online Defragmentation Suman Bhunia Vahid Behzadan and Shamik Sengupta Supported by NSF CAREER grant CNS 1346600 Outline Introduction Motivation Some Related Work ID: 574011
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Enhancement of Spectrum Utilization in Non-Contiguous DSA with Online Defragmentation
Suman Bhunia, Vahid Behzadan and Shamik Sengupta
Supported by NSF CAREER grant CNS #1346600Slide2
Outline
IntroductionMotivation
Some Related Work
Proposed Model
AlgorithmsPrototype Performance EvaluationConclusion and Future Work
2Slide3
Why Dynamic Spectrum Access?
3Slide4
Non-Contiguous DSA
Dynamic RF environment Dynamic spectrum requirementSometimes single spectrum opportunities are not adequate to support users’ requirementsAllocation of spectrum in the form of non-contiguous blocks4Slide5
Outline
IntroductionMotivation
Some Related Work
Proposed Model
AlgorithmsPrototype Performance EvaluationConclusion and Future Work
5Slide6
NC-DSA
PUs have priority in spectrum acquirementSUs (red, green and blue) change their spectrum with PU activityIncreases fragmentsOverhead due to guard bands (yellow)Defragmentation minimizes the spectrum wastage
6Slide7
Problem Statement
Sinc type pulses lead to large sidelobes - out of band transmission Guard bands to protect fragmentsOpportunistic NC spectrum allocation increases number of spectrum fragmentsIncreasing fragments increase spectrum wastage by guard bands
This paper investigates
The cost of wastage due to guard bands
Overhead of coordination in NC DSAMitigation techniquesProposes Online Spectrum Defragmentation as an effective solution to wastage of spectrum due to guard bands.7Slide8
Outline
IntroductionMotivation
Some Related Work
Proposed Model
AlgorithmsPrototype Performance EvaluationConclusion and Future Work
8Slide9
Some related works
Aggregation Aware Spectrum Assignment (AASA) 1All users require the same amount of spectrumUses first-fit approach for channel assignmentsMaximum Satisfactory Algorithm (MSA
)
2
users may have different spectrum requirementsUses best-fit algorithmChannel Characteristic Aware Spectrum Aggregation algorithm (CCASA) 3Considers the heterogeneity of data carrying capacity in spectrumUses sliding window method9
D. Chen, Q. Zhang, and W.
Jia
, “Aggregation aware spectrum assignment in cognitive ad-hoc networks,” in 3
rd
International Conference on
Cognitive
Radio Oriented Wireless Networks and Communications, 2008.
CrownCom
2008,
pp. 1–6, IEEE, 2008.
F. Huang, W. Wang, H. Luo, G. Yu, and Z. Zhang, “Prediction based spectrum aggregation with hardware limitation in cognitive radio networks,” in IEEE 71st Vehicular Technology Conference (VTC 2010-Spring),
2010,
pp. 1–5, IEEE, 2010.
J. Lin, L. Shen, N.
Bao
, B. Su, Z. Deng, and D. Wang, “Channel characteristic aware spectrum aggregation algorithm in cognitive radio networks,” in IEEE 36
th
Conference on Local Computer Networks (LCN),
2011,
pp. 634–639, IEEE, 2011.Slide10
Related Works…
Jello: A MAC Overlay for Dynamic Spectrum Sharing1 Distributed NC OFDM prototypeDistributed defragmentation triggered by other SU departureLimited sensing windowHomogenous spectrum
10
L
. Yang, W.
Hou
, L. Cao, B. Y. Zhao, and H. Zheng, “Supporting demanding wireless applications with frequency-agile radios.,” in Pro-
ceedings
of the 7th USENIX Conference on Networked Systems Design and Implementation, NSDI 2010, pp. 65–80, 2010. Slide11
Outline
IntroductionMotivation
Some Related Work
Proposed Model
AlgorithmsPrototype Performance EvaluationConclusion and Future Work
11Slide12
Problem Formulaion
Data Rate matrixSpectrum assignment matrix: Define three (N×C) matrices: Data-subcarrier assignment matrix (D)
Pilot-subcarrier
assignment
matrix (P)Guard-subcarrier assignment matrix (G)12Slide13
Problem Formulaion…
Throughput achieved:Cross Channel Interference Matrix:Constraint for interference:
13Slide14
Optimization Problem
14
Interference
Demand satisfaction
Prevent overlappingTransmission BWPower consumptionInterface limitationSlide15
Theorem: The throughput maximization problem is NP-hard even if there is no PU present.
Assume No cross channel interferenceEach subcarrier provides same data rateSUs have different data rate demandAn SU can be
allocated with
spectrum
iff its demand is metThe goal is to maximize total throughput of the systemreduction of the 0-1 knap sack problem15Slide16
Outline
IntroductionMotivation
Some Related Work
Proposed Model
AlgorithmsPrototype Performance EvaluationConclusion and Future Work
16Slide17
Centralized Spectrum AllocationCentral controller supervises
the spectrum allocationUses dedicated out-of-band common control channel (CCC)SUs periodically sense the spectrum and send the spectrum usage map to the controllerSU also notifies the controller
of its
throughput
requirementsController has two states:Steady stateArrangement state17Slide18
Centralized Spectrum Allocation
18Slide19
Distributed Method
19Slide20
Semi Centralized Method
20Slide21
Outline
IntroductionMotivation
Some Related Work
Proposed Model
AlgorithmsPrototype Performance EvaluationConclusion and Future Work
21Slide22
Prototype Schema
GNURadio controlled USRP200 KHz band 256 subcarriers of 781.25 Hzminimum of 28 subcarriersFiltering and windowing – degrade OFDM signal, not agile enough
Unutilized
subcarriers are required as guard bands
22Slide23
Spectrum Usage
23
Received Signal
NC spectrum allocation of
B
:
NC spectrum allocation of
A
:Slide24
Outline
IntroductionMotivation
Some Related Work
Proposed Model
AlgorithmsPrototype Performance EvaluationConclusion and Future Work
24Slide25
Simulation Parameters
25Slide26
Simulation Results
26Throughput obtained for the network
compare
with CCASA
CCSA does not consider the waste of spectrumthroughput increases linearly until a saturation pointSlide27
Simulation Results
Spectral efficiency for centralized method
27Slide28
Simulation Results
28Throughput achieved for the entire network
Significantly
better performance in comparison with
JelloWith high no. of nodes, the throughput of decentralized method decreasesSlide29
Outline
IntroductionMotivation
Some Related Work
Proposed Model
AlgorithmsPrototype Performance EvaluationConclusion and Future Work
29Slide30
ConclusionOnline Defragmentation
is proposed as a method of increasing spectrum utilizationEfficiency of this method was investigated in three different network scenarios:InfrastructureDistributed Semi-centralized.
proof-of-concept prototype
Regardless
of scenario, defragmentation provides better performance30Slide31
Future WorksComplete implementation of the proposed algorithms in
testbedOptimization of guard bandwidthHeterogeneity of subcarriers Adaptive defragmentation based on spatial considerations31Slide32
32
Thank You!Slide33
33
AppendixSlide34
34