International Seminar on Intelligent Technology and Its
Applications (ISITIA) 2016
July, 28th-30th 2016
Lombok - Indonesia
Presented by :
Chaeriah Bin Ali Wael
( chae003@lipi.go.id / chaeriah.wael10@gmail.com)
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Outline
• Motivation
• Eigenvalue based
detection
•
Maximum-minimum
Eigenvalue (MME)
Detection
•
•
Simulation Results
Slide-2
Conclution
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Motivation
Fixed spectrum
allocation policy causes
spectrum scarcity
Dynamic spectrum
allocation policy
become a solution
Cognitive
Radio
Slide-3
• CR optimizes the use of temporally
unused radio-frequency (RF)
spectrum without causing harmfull
interference to licensed users
(primary users)
• Spectrum sensing ensures the the
licensed users are well-protected by
providing the awareness of the
spectrum usage in the surrounding
environment
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Motivation
• Spectrum sensing techniques :
o Energy detection
o Matched filter detection
o Cyclostationary detection
•
Commonly used due to its simplicity
and applicability as well as its low
computational and implementation
costs.
Energy detection drawbacks :
o Depends on accurate estimation of noise power which is
difficult to obtain under low SNR environment.
o Suffer from noise uncertainty.
•
Eigenvalue based detection more robust under low SNR
environtment
Slide-4
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Eigenvalue based Detection
•
Block diagram :
x[n]
Covariance
matrix
Eigenvalues
calculation
Test
against
threshold
H0 / H1
Threshold
calculation
Slide-5
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Maximum-minimum Eigenvalue (MME)
Detection
Maximum-minimum Eigenvalue (MME) detection :
o Build covariance matrix of the sampled signal
o Obtain eigenvalues of covariance matrix using SVD
o Calculate the decision threshold γ for the given Pf :
q(1)
q(2)  q( L) 



q
(
2
)
q
(
3
)

q
(
L

1
)
 
Q




 


 q( N ) 
q( N  L  1)



L
N  L
2
N 
2

1 



N  L
NL
1
6

2
3

F (1  Pf ) 


1
1
o Generate test statistic of MME detection and make a
decision :
max   , H 0
TMME 

min   , H 0
Slide-6
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Simulation Results
Pd vs SNR
• It is observed that higher
1
0.9
Probability of detection (Pd)
0.8
Pf = 0.001
Pf = 0.01
Pf = 0.1
Pf gives higher Pd.
•
0.7
0.6
0.5
0.4
0.3
0.2
Above -6 dB, the
detection probability
reaches 1 for all Pf
values.
0.1
0
-25
-20
-15
-10
-5
0
SNR (dB)
(a) Pd vs SNR curve with Pf = 0,001, 0,01, 0,1
Slide-7
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Simulation Results
Pd vs SNR With different number of samples (Ns)
Pd vs SNR of vary number of smooting factor (L)
1
1
Ns = 250
Ns = 500
Ns = 1000
0.9
0.8
Probability of detection (Pd)
0.8
Probability of detection (Pd)
L=8
L = 16
L = 24
0.9
0.7
0.6
0.5
0.4
0.3
0.7
0.6
0.5
0.4
0.3
0.2
0.2
0.1
0.1
-25
-20
-15
-10
-5
SNR (dB)
(b) Pd vs SNR curve for N = 250, 500, 1000.
•
Slide-8
0
0
-25
-20
-15
-10
-5
0
SNR (dB)
(c) Pd vs SNR curve for L = 8, 16, 24
Higher number of samples (N) and smoothing factor (L) give
higher probability of detection.
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Simulation Results
Pd vs SNR of energy detection and eigenvalue based detection
• At certain range value of
0.9
0.8
Probability of detection (Pd)
SNR (-25 dB to -16 dB),
MME outperform energy
detection
1
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-25
Slide-9
energy detection
eigenvalue based detection
-20
-15
-10
Probability of false alarm (Pf)
-5
0
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Simulation Results
with roll-off factor
= 0,35, raised cosine
filter gives better
performance root
raised cosine.
0.9
rectangular
Raised Cosine
Root Raised Cosine
0.8
Probability of detection (Pd)
•
Pd vs SNR
1
0.7
0.6
0.5
0.4
0.3
0.2
0.1
-25
-20
-15
-10
-5
0
SNR (dB)
Slide-10
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Conclusions
•
The derived numerical simulation showed that higher SNR
value and threshold parameters setting give better probability
of detection.
•
MME detection outperform energy detection in low SNR
environtment.
•
Different pulse shaping filters are also investigated.
Slide-11
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
B. Benmammar and A. Amraoui. Radio resource allocation and dynamic spectrum access. John Wiley & SoN, 2013.
D. Cabric, A. Tkachenko and R.W. Brodersen. “Spectrum sensing measurements of pilot, energy, and collaborative detection.” Proc.
of IEEE Military CommunicatioN Conference (MILCOM) 2006, pp. 1-7, Oct. 2006.
T. Yucek and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio ApplicatioN”, IEEE Commun. Surveys &
Tuts., vol. 11, no.1, pp. 116–130, 2009.
Haykin, S., “Cognitive radio: Brain-empowered wireless communicatioN,” IEEE J. Sel. Areas Commun, vol. 23, no. 2, pp. 201–220,
Feb. 2005.
M. Matinmikko, H. Sarvanko, M. Mustonen, and A. Mammela, “Performance of spectrum sensing using Welch's periodogram in
Rayleigh fading channel,” Proc. of IEEE conference on Cognitive Radio Oriented Wireless Networks and CommunicatioN 2009.
(CROWNCOM'09), pp. 1-5, 2009.
S. Atapattu, C. Tellambura and H. Jiang, “Spectrum sensing via energy detector in low SNR,” Proc. of IEEE International
Conference on Communication (ICC), pp. 1-5, June 2011.
S. Kapoor, S.V.R.K. Rao, and G. Singh, “Opportunistic spectrum sensing by employing matched filter in cognitive radio network,”
Proc. Of IEEE International Conference on Communication Systems and Network Technologies (CSNT), pp. 580-883, June 2011.
Xu, Shiyu, Zhijin Zhao, and Junna Shang. “Spectrum sensing based on cyclostationarity,” Proc. of IEEE International Workshop on
Power Electronics and Intelligent Transportation System, 2008 (PEITS'08). pp. 171-174, Aug. 2008.
D. Bhargavi and C.R. Murthy, “Performance comparison of energy, matched-filter and cyclostationary-based spectrum sensing,”
Proc. of IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1-5, June 2010.
10.Y. H. Zeng and Y.C. Liang, “Maximum-minimum eigenvalue detection for cognitive radio,” Proc. of IEEE 18th Int. Symp. Pers.,
Indoor Mobile Radio Commun.(PIMRC) Sep. 2007.
11.Y.H. Zeng, L.K. Choo and Y.C. Liang: Maximum eigenvalue detection: theory and application, IEEE International Conference on
CommunicatioN, pp. 5076-5080, 2008.
12.Y. H. Zeng and Y.C. Liang, “Eigenvalue-based spectrum sensing algorithms for cognitive radio,” IEEE TraNactioN on
CommunicatioN, vol. 57, no. 6, pp. 1784–1793, 2009.
13.T. J. Lim, R. Zhang, Y. C. Liang and Y. Zeng, “GLRT-Based Spectrum Sensing for Cognitive Radio,” IEEE Global
TelecommunicatioN Conference pp. 1-5, 30 Nov – 4 Dec, 2008.
Slide-12
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Thank You!
Questions ?
Slide-13
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Cognitive Radio
•
A concept of Cognitive Radio (CR) has been proposed as a
promising technology to enable dynamic spectrum access.
•
The basic idea of cognitive radio is to allow unlicensed users
(called secondary users) to access licensed band spectrum
without interfering licensed users (called primary users).
•
Cognitive radio adapts to surrounding RF environment by
spectrum sensing. It identifies the presence of a signal in a
noisy environment.
Cognitive Radio = Sense + Learn + Adapt + Use
Slide-14
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Cognitive Radio
• Cognitive cycle
We are here!
Transmitted Signal
Radio Environment
RF
Stimuli
Spectrum
Mobility
Primary User Detection
Spectrum
Sensing
Decision Request
Spectrum Hole
Spectrum
Sharing
Channel Capacity
Spectrum
Decision
Slide-15
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Spectrum Sensing
•
Spectrum sensing aims to :
Monitor the available
spectrum bands.
Capture spectrum bands
information.
Detect the spectrum holes.
•
To provide high spectral
utilization, spectrum sensing
should be capable to sense a large
bandwidth spectrum.
 Wideband spectrum sensing
Slide-16
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences
Parameter of performance analysis
•
Performance analysis : ROC curve  Pd vs. desired Pf
Pd
Pf
Where Pf = Prob(T(X) > λ|H0)
Pd = Prob(T(X) > λ|H1)
Slide-17
PFA : Probability of the False Alarm
PD : Probability of Detection
γ : the threshold
Research Center for Electronics and Telecommunication
Indonesian Institute of Sciences