Waleed Session 1Presntation
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Transcript of Waleed Session 1Presntation
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8/3/2019 Waleed Session 1Presntation
1/19
Fully Distributed Cooperative Spectrum Sensing forCognitive Radio Ad Hoc Networks
Waleed Ejaz
[email protected] Network Systems Lab
Sejong UniversityRepublic of Korea
December 19, 2011
Co-authors: Najam ul Hasan and Hyung Seok Kim
Sejong University
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Outline
Abstract
Introduction
Related work System model
Gradient based fully distributed approch
Simulation Results Conclusion
Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Ad Hoc Networks
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Abstract
Cognitive radios are intelligent with capability of sensing theradio environment
Recently cognitive radios are considered for ad hoc environment. Cognitive radio ad hoc network (CRAHNs) have several unique
features. Ad hoc networks are dynamic in nature and have no central
entity for data fusion. Cognitive radio ad hoc networks needs fully distributed
cooperative spectrum sensing. A fully distributed cooperative spectrum sensing is proposed for
cognitive radio ad hoc networks. The proposed scheme is analyzed from the perspective of reliable
sensing and energy consumption.
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Introduction
Recent measurements shows that 15-85% ofthe allocated spectrum is used sporadically
This scarcity is due to spectrum managementpolicies
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Cognitive Radio Networks
A Cognitive Radio is a radio that can changeits transmitter parameters based on interactionwith the environment in which it operates. (Fed
eral Com Commission05)
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Essential Task
The most essential task of CR is to detect thepresence or absence of PU.
Prime concern of spectrum sensing is about
two things: primary system should not be disturbed by CRU co
mmunication
spectrum holes should be detected efficiently for re
quired throughput and quality of service (QoS)
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Infrastructure based network vs CRAHNs
1) Local
observation
2) Decision at
fusion center
3) Reconfiguration4) Reconfiguration
1) Local
observation
2) Cooperation
3) Decision
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Basic Hypothesis Model
The signal r(t) received by the CR user is
1
0
)()(
)()(
Htnths
Htntr
where n(t) AWGN (Additive White Gaussian Noise)
s(t) Transmitted signal of the primary user
h Amplitude gainof the channel
H0 Null hypothesis No licensed user signal in a certain spectrumband.
H1 Alternative hypothesis There exists some licensed user signal.
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Spectrum Sensing Techniques
Most common transmitter detection techniques are:
Energy Detection
Matched Filter detection
Cyclostationary Feature Detection
Only energy detection is considered as local spectrumsensing technique for implementation for the sake of
simplicity.
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Network of CRUs
Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Ad Hoc Networks
7
1
8106
43
2
9
5NodeBidirectional links
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Consensus based scheme
This scheme is based on recent advances in consensus algorithmsthat have taken inspiration from self-organizing behavior of
animal groups such as birds, fish, ants, honey bees and others.
A CRU needs only to set up local interactions without a centralize
d node in cognitive radio ad hoc networks.
Limitation of existing technique is that choice of step size depends on maximum number of neighbors of a node in network.
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Assumptions in proposed scheme
The following are two assumptions for the proposed scheme.
There is full duplex wireless connection between
CRU and each of its desired neighbor and remainsconnected until the energy converges
Network topology is modeled as fixed graph.
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The gradient-based algorithm for this purpose can be stated as:
where xm(i) is the energy received neighbors. The converged valueis then compared with the predetermined threshold . Each CRUgets the final decision locally as
mm
mnnn
ix
ixixixix
)(
)()()()1(
.,0
,,1 *
otherwise
xifDecision
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Algorithm
1: Initially CRUn do2: calculate En3: xn(0)=En4: emit xn(0) to neighbours5: for CRUn do6: while (all xn do not converge to any common value x*)7: ifreceive xmfrom neighbour mthen
8: ifxm xn> Dthen9: Exclude mfrom the list of neighbours10: else11: calculate updated xnusing eq on last slide12: end if13: end if14: end while15: ifxn> then16: PU is present17: else18: PU is absent19: end if20: end for
Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Ad Hoc Networks
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Simulation Results (1)
Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Ad Hoc Networks
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Simulation Results (2)
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Summary
We proposed a fully distributed and scalable cooperativespectrum sensing scheme for CRAHNS.
CRUs exchange the local sensing information to neighbors andconverge after some iteration without the aid of any centralentity.
The point of convergence is compared with a predeterminedthreshold to decide about the presence or absence of primaryuser.
The major advantage of the proposed scheme is that it does not
require any prior knowledge about the degree of the networkthereby saving energy consumption.
It is shown in results that the proposed scheme can achieve theperformance as good as the consensus-based scheme with lessenergy consumption.
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Acknowledgement
Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Ad Hoc Networks
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QUESTIONS
Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Ad Hoc Networks