sparse spectrum sensing in infrastructure-less cognitive radio networks via binary consensus...
TRANSCRIPT
Background System Model
Sparse Spectrum Sensing inInfrastructure-less Cognitive Radio
Networks via Binary ConsensusAlgorithms
Mohamed Seif1
1Wireless Intelligent Networks Center (WINC), Nile University, Egypt
January, 2016
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 1
Background System Model
Sampling Theory
Shannon/Nyquist sampling theorem:
No information loss if wesample at 2x signal bandwidth
DSP revolution: Sample first and askquestions later (Compression,Storage, ..., etc)
Increasing pressure on DSPhardware, algorithms
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 2
Background System Model
Compressive Sensing
Compressive sensing (CS) theory combines the signalacquisition and compression steps into a single step.The main requirement is that the acquired data is sparse insome transform domain.
x ≈ ∑
K<<N largest termsαiψi (1)
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 3
Background System Model
Compressive Sensing
Compressive sensing (CS) theory combines the signalacquisition and compression steps into a single step.The main requirement is that the acquired data is sparse insome transform domain.
x ≈ ∑
K<<N largest termsαiψi (1)
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 3
Background System Model
Compressive Sensing Formulation
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 4
Background System Model
Compressive Sensing Formulation
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 5
Background System Model
Compressive Sensing Formulation
Signal recovery:
minx∈RN
∥x∥1 s.t . ∥y − φx∥2 ≤ ε (2)
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 6
Background System Model
CS for Spectrum Sensing
frequencyN channel sub-bands
Empty sub-band Occupied sub-band
Figure: Sparsity Nature of Spectrum Occupation by PUs.
XN×M = RN×N × (GM×N)T (3)
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 7
Background System Model
System Model
CR1
CR3
CR2
CR4
Figure: Infrastructure-less CRN.
Vector Consensus Problem
bj(k) = Dec(1M(b(0) +
1Kp
K∑
t=1B(t)aT
j (t))) (4)
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 8
Background System Model
System Model
CR1
CR3
CR2
CR4
Figure: Infrastructure-less CRN.
Vector Consensus Problem
bj(k) = Dec(1M(b(0) +
1Kp
K∑
t=1B(t)aT
j (t))) (4)
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 8
Background System Model
System Model
CR1
CR3
CR2
CR4
Figure: Infrastructure-less CRN.
Vector Consensus Problem
bj(k) = Dec(1M(b(0) +
1Kp
K∑
t=1B(t)aT
j (t))) (4)
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 8
Background System Model
Numerical Results
Simulation Results
Parameter RealizationN 200T 30M 12P 4
dmin 10 mA 1000 m ×1000 mK 10α 2
No. iterations 100
Table: Simulation Parameters.
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 9
Background System Model
Numerical Results
Simulation Results
0 5 10 15 20 250.4
0.5
0.6
0.7
0.8
0.9
1
SNR (dB)
Pd
Centralized − Majority RuleInfrastructure−less, K = 10Infrastructure−less, K = 11Infrastructure−less, K = 12
Figure: Comperison between two architectures (Fusion based vsInfrastructure-less).
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 10
Background System Model
Numerical Results
Simulation Results
0 5 10 15 20 250.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
SNR (dB)
Pd
p=0.3p=0.5p=0.8
Figure: Effect of link quality on probability of detection.
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 11
Background System Model
Numerical Results
Simulation Results
0 5 10 15 20 250.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
SNR (dB)
Pd
T = 30T = 50T = 90
Figure: Effect of number of measurements on probability of detection.
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 12
Background System Model
Numerical Results
Simulation Results
1 2 3 4 5 6 7 8 9 100.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
Pd(k
)
k
p = 0.2p = 0.8
Figure: Effect of link quality - (not final)
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 13
Background System Model
Numerical Results
Simulation Results
1 2 3 4 5 6 7 8 9 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
k
Pd(k
)
t=150t=90
Figure: Effect of number of measurements - (not final)
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 14
Background System Model
Numerical Results
Thank You!
Mohamed Seif Nile University
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms 15