Applications of Wavelet Transform and Artificial
Neural Network in Digital Signal Detection for
Indoor Optical Wireless Communication
Sujan Rajbhandari
1
Sujan Rajbhandari
Supervisors
Prof . Maia Angelova
Prof. Z. Ghassemlooy
Prof. Jean

Pierre Gazeau
Optical
Wireless
Communication
Sujan Rajbhandari
2
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
Transmission Format
Transmitted signal
‘1’ presence of an optical pulse
‘0’ absence of an optical pulse
Sujan Rajbhandari
0
1
1
0
0
0
1
1
0
1
Links
Sujan Rajbhandari
4
Non

LOS
Multipath Propagation
Intersymbol interference (
ISI
)
Difficult to achieve high data
rate if ISI is not mitigated.
Rx
Tx
LOS
LOS
No multipath propagation
Noise and device speed
are limiting factors
Possibility of blocking
Tx
Rx
Received Signal
Sujan Rajbhandari
5
Non

LOS
LOS
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.
Sujan Rajbhandari
6
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
8
Received signal
‘1’ signal + interference
‘0’ interference only (noise and intersymbol
interference (ISI)) .
Interference only signal + interference
Sujan Rajbhandari
Receiver
from
the
Viewpoint of Statistics
9
Testing a
Null Hypothesis
of
a)
Received signal is
interference only
against
b)
Alternative Hypothesis of received signal is
signal
plus interference
Sujan Rajbhandari
Problem of Feature Extraction
and Pattern Classification
10
Receiver Block diagram
Optical
Receiver
Wavelet
Transform
Artificial
Neural Network
Threshold
Detector
Feature
Extraction
Pattern
Classification
Sujan Rajbhandari
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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11
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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12
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)
Sujan Rajbhandari
13
Continues Wavelet Transform
Where
are
wavelets
and
s
and
τ
are
scale
and
translation
.
Translation
time resolution
scale
frequency resolution
Wavelets are generated from scaling and translation
the
Mother wavelet.
CWT of Signal
f
(
t
) and reconstruction is given by
Discrete Wavelet Transform
•
Dyadic
scales and positions
•
DWT coefficient can efficiently be obtained by
filtering
and
down sampling
1
1
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
Artificial Neural Network
Fundamental unit :
a
neuron
Based on biological
neuron
Capability to learn
Sujan Rajbhandari
16
b
w
n
y
x
1
f
(
.
)
∑
w
1
Output
x
n
.
.
.
Artificial Neural Network
Input layer , hidden layer(s) and
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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17
Feature Extraction:
Discrete Wavelet Transform
Sujan Rajbhandari
18
DWT of Interference only
DWT of signal +Interference
•
Significant difference in approximation coefficient ,a
3
.
•
No difference in other details coefficients. (Details coefficient are
due to the high frequency component of signal , mainly due to noise.)
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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19
De

noised Signal
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20
LOS Links
Non

LOS Links
•
Denoising effectively removes high frequency component.
•
Equalization is necessary for non

LOS links
•
Identical performance for both de

noising approaches.
21
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.
Results
Sujan Rajbhandari
22
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
D
rms
of 10ns
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
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.
Sujan Rajbhandari
24
25
Thank You
Discussions
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