Transcript Optical Wireless Communication using Digital Pulse

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Applications of Wavelet Transform and Artificial
Neural Network in Digital Signal Detection for
Indoor Optical Wireless Communication
Sujan Rajbhandari
Supervisors
Prof . Maia Angelova
Prof. Fary Ghassemlooy
Prof. Jean-Pierre Gazeau
Sujan Rajbhandari
Optical Wireless Communication
 Light as the carrier of information
 Also popularly known as free space
optics (FSO) or Free Space Photonics
(FSP) or open-air photonics .
 Indoor or outdoor
Sujan Rajbhandari
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Transmission Format
Transmitted signal
 ‘1’
 ‘0’
presence of an optical pulse
absence of an optical pulse
Transmitted OOK
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Ampitude
0.8
0.6
0 1 1 0 0 0 1 0 1 1
0.4
0.2
0
0
2
4
6
Normalized Time
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Links
Non-LOS
LOS
 LOS
Rx
Tx
Tx
Rx
 No multipath propagation
 Noise and device speed  Multipath Propagation
are limiting factors
 Intersymbol interference (ISI)
 Possibility of blocking
 Difficult to achieve high data
rate if ISI is not mitigated.
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Received Signal
Non-LOS
LOS
Received signal for non-LO OOK
Received OOK for LOS links
1
1
0.8
0.8
0.6
0.6
Amplitude
Amplitude
1.2
0.4
0.2
0
0.4
0.2
0
-0.2
-0.2
-0.4
-0.4
0
2
4
6
Normalized Time
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0
2
4
6
Normalized Time
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Classical Digital Signal Detection
 Set a threshold level.
 Compared the received signal with the threshold
level
 Set ‘1’ if received signal is greater than threshold
level
 Set ‘0’ is received signal is less than threshold
level.
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Classical signal detection
techniques: Assumptions
 The statistical of noise is known.
 Maximise the signal to noise ratio for
unknown noise with known statistics.
 Channel characteristics are known( at
least partially ) and generally assume to
be linear.
Digital signal Reception:
Problem of feature extraction and pattern
classification
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Received signal
 ‘1’
signal + interference
 ‘0’
interference only (noise and intersymbol
interference (ISI)) .
2.5
1.5
2
1
Amplitude
Amplitude
1.5
0.5
1
0.5
0
0
-0.5
-0.5
-1
0
0.2
0.4
0.6
0.8
1
Normalized Time
0
0.2
0.4
0.6
0.8
1
Normalized Time
Interference only
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signal + interference
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Receiver from the Viewpoint of Statistics

Testing a Null Hypothesis of
a)
Received signal is interference only
against
b)
Alternative Hypothesis of received signal is signal
plus interference
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Problem of Feature Extraction
and Pattern Classification
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 Receiver Block diagram
Optical
Receiver
Wavelet
Transform
Feature
Extraction
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Artificial
Neural Network
Pattern
Classification
Threshold
Detector
Time- Frequency analysis
Fourier Transform
 Time-frequency mapping
 What frequencies are present in a signal but
fails to give picture of where those
frequencies occur.
 No time resolution.
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Time- Frequency analysis
Windowed Fourier Transform (Short time Fourier
transform)
 Chop signal into equal sections
 Find Fourier transform of each section
Disadvantages
 Problem how to cut a signal
 Fixed time and frequency resolution
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Time- Frequency analysis
Continuous Wavelet Transform (CWT)
 Vary the window size to vary resolution
(Scaling).
 Large window for precise low-frequency information,
and shorter window high-frequency information
 Based on Mother wavelet.
 Mother Wavelet are well localised in time.(Sinusoidal
wave which are the based of Fourier transform
extend from minus infinity to plus infinity)
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Continues Wavelet Transform
CWT of Signal f(t) and reconstruction is given by
 ( s, )   f (t ) *s , (t )dt
f (t )     ( s, ) s , (t )dds
 Where  s, (t ) are wavelets and s and τ are scale and
translation.
 Translation
time resolution

scale
frequency resolution
 Wavelets are generated from scaling and translation
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t 
the Mother wavelet.
 (t ) 
 (
)
s ,
s
s ,
s
Discrete Wavelet Transform
• Dyadic scales and positions
• DWT coefficient can efficiently be obtained by filtering and
down sampling1
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Mallat, S. (1989), "A theory for multiresolution signal decomposition: the wavelet representation," IEEE Pattern Anal. and
Machine Intell., vol. 11, no. 7, pp. 674-69
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Artificial Neural Network
 Fundamental unit : a
neuron
 Based on biological
neuron
 Capability to learn
b
x1 w1
.
.
.
xn
n
y  f (b   xk .wk )
k 1
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wn
∑
f(.)
Output
y
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Artificial Neural Network
 Input layer , hidden layer(s) and
Input
Layer
Hidden Hidden
Layer 1 Layer 2
output layer
 Extensively used as a classifier
 Supervised and unsupervised
learning.
 Weight are adjust by
comparing actual output and
target output
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Output
Feature Extraction:
Discrete Wavelet Transform
DWT of Interference only
DWT of signal +Interference
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a3
a3
1
0
-1
2
4
6
8
10
12
14
16
18
20
2
4
6
8
10
12
14
16
18
20
0
5
10
15
20
25
30
35
40
0
-1
10
20
30
40
50
60
70
80
10
12
14
16
18
20
0
2
4
6
8
10
12
14
16
18
20
0
5
10
15
20
25
30
35
40
0
10
20
30
40
50
60
70
80
0
-0.5
0
8
0.5
d1
d1
1
6
0
-0.5
0
4
0.5
d2
d2
1
2
0
-0.5
0
0
0.5
d3
d3
0
-1
1
0
0
1
-1
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• Significant difference in approximation coefficient ,a3.
• No difference in other details coefficients. (Details coefficient are
due to the high frequency component of signal , mainly due to noise.)
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Denoising
 The high frequency component can be removed or
suppressed.
 Two Approach Taken
1. Threshold approach in which the detail coefficients
are suppressed by either ‘hard’ or ‘soft’
thresholding.
2. Coefficient removal approach in which detail
coefficients are completely removed by making it
zero.
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De-noised Signal
Non-LOS Links
LOS Links
Denoised signal for LOS links
1.5
Denoised signal for non-LOS links
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Received signal
Denoising
0.8 (Threshold Approach)
Denoised Signal
(Threshold approach)
1
Denoised Signal
(Coeff. Removal Approach)
0.5
Amplitude
Amplitude
0.6
Denoised Signal
Coeff. Removal Approach
0.4
0.2
0
Received Signal
0
-0.2
-0.4
-0.5
0
2
4
6
8
10
0
2
4
6
Normalized Time
Normalized Time
•Denoising effectively removes high frequency component.
•Equalization is necessary for non-LOS links
•Identical performance for both de-noising approaches.
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Artificial Neural Network :
Pattern Classifier
 Artificial Neural Network can be trained to
differentiate the interference from signal plus
interference.
 DWT are fed to ANN.
 ANN is first trained to classify by providing
examples.
 ANN can be utilized both as a pattern
classifier and equalizer.
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Results
 The Coefficient
removal approach (CRA)
of denoising gives the
best result.
 Easier to train ANN
using CRA as the DWT
coefficients are removed
by 8 folds if 3 level of
DWT is taken.
 Effective for detection
and equalization.
Figure: The Performance of On-off Keying at 150Mbps
for diffused channel with a Drms of 10ns
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Comparison with traditional methods
•Maximum performance of
8.6dBcompared to linear
equalizer
• performance depends on the
mother wavelets.
• Discrete Meyer gives the best
performance and Haar the worst
performance among studied
mother wavelet
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Conclusion
 Digital signal detection can be reformulated as
feature extraction and pattern classification.
 Discrete wavelet transform is used for feature
extraction.
 Artificial Neural Network is trained for pattern
classification.
 Performance can further be enhance by denoising
the signal before classifying it.
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Thank You
Discussions