Transcript Optical Wireless Communication using Digital Pulse

Performance of Discrete Wavelet Transform – Artificial
Neural Network Based Signal Detector/Equalizer for
Digital Pulse Interval Modulation in Practical Indoor
Optical Wireless Links
Z. Ghassemlooy, S. Rajbhandari and M. Angelova
School of Computing, Engineering & Information Sciences,
University of Northumbria,
Newcastle upon Tyne, UK
http://soe.unn.ac.uk/ocr/
Outline
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Optical wireless – introduction
Modulation Techniques- Overview
Mutipath induces ISI
Unequalized power penalty
Wavelet-ANN receiver
Final comments
3
What Optical Wireless Offers ?
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Abundance bandwidth
Free from electromagnetic interference
High data rate
No multipath fading
High Directivity.
Secure data transmission
Spatial confinement.
Low cost of deployment
License free operation
Quick to deploy
Compatible with optical fibre
Simple transceiver design.
Small size, low cost component and low power consumptions.
4
Modulation Techniques
 On-off keying (OOK): the most basic, simple to implement but
requires a high average optical power.
 Pulse position modulation (PPM): The most power
efficient but require high bandwidth, susceptible to the multipath
induced intersymbol interference (ISI).
 Differential PPM (DPPM) and digital pulse interval
modulation (DPIM): Variable symbol length, built-in symbol
synchronization; improved throughputs and efficient utilization of
the available bandwidth compared to PPM.
 Dual header pulse interval modulation (DH-PIM):
Variable symbol length , built-in symbol synchronization; the most
efficient utilization of channel capacity compared to OOK, PPM and
DPIM.
Baseband Modulation Techniques
Normalized Power and Bandwidth
Requirement
Normalized bandwidth requirement
20
 PPM the most power efficient
while requires the largest
bandwidth.
18
16
14
12
10
8
 DH-PIM2 is the most bandwidth
efficient.
6
4
2
0
2
OOK
3
4
5
6
7
8
Normalized Power Requirement (dB)
Bit resolution, M
 DH-PIM and DPIM shows almost
identical bandwidth requirement
and power requirement.
0
-2
-4
-6
 There is always a trade-off
between power and bandwidth.
-8
-10
-12
-14
-16
2
3
4
5
6
Bit Resolution, M
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Indoor Optical Wireless Links
 The key issues are:
- The eye safety
- shift to a higher wavelength of 1550 nm where the eye retina
is less sensitive to optical radiation
- power efficient modulation techniques.
- Mobility and blocking
- Use diffuse configuration instead of line of sight,
but at cost of
-
reduced data rate
increased path loss
multipath induced inter-symbol-interference (ISI)
High noise at receiver due to artificial light.
Effect of Artificial Light
 Dominant noise source at low data
rate.
 Interference produce by fluorescent
lamp driven by electronic ballasts can
cause serious performance
degradation at low data rate.
 The effect of artificial light is
minimised at the receiver using
combination of the optical band
pass filter and electrical low pass
filter.

: Optical power spectra of common ambient infrared
sources. Spectra have been scaled to have the same maximum
value.
1Figure
1J.
At the high data rate, the ISI is the
limiting factor in the performance of
the system instead of artificial light.2
M. Kahn and J. R. Barry, Proceedings of IEEE, vol. 85, pp. 265-298, 1997.
J. C. Moreira, R. T. Valadas, and A. M. d. O. Duarte, IEE Proceedings -Optoelectronics, vol. 143, pp. 339-346, 1996.
2A.
9
Intersymbol Interference (ISI)
 Limiting factor in achieving high data
rate in diffuse links.
LOS
 ISI is due to broadening of pulse.
Diffuse
Diffuse shadowed
 Diffuse links are characterised by RMS
delay spread.
 The impulse response in Ceiling
bounce model is given by1 :
60.1Drms 
LOS shadowed
1.2
1
6
0.8
t  0.1Drms 
7
u (t )
where u(t) is the unit step function
1- J. B. Carruthers and J. M. Kahn, IEEE Transaction on Communication,
vol. 45, pp. 1260-1268, 1997.
Amplitude
h (t ) 
Received signal for non-LOS Links
0.6
0.4
0.2
0
-0.2
-0.4
0
2
4
6
Normalized Time
8
10
Unequalized Performance
Input
bits, ai
DPIM
encoder
Transmitter
filter
p(t)
Receiver
Channel
Transmitter
X(t)
LavgPavg
Multipath
channel
h(t)
Z(t) Unit energy y(t)
S(t)
filter r(t)
(matched to
p(t))
n(t)
yi
Output
Bits, aˆi
sample
R
 The discrete-time impulse response of the cascaded system is
c  p(t )  h(t )  r (t )
t  kT
k
b
 In non-LOS links, ck contains a zero tap, a single precursor tap (with the
largest magnitude) and possibly multiple postcursor taps.
 The optimum sampling point is at the end of each slot period Ts for
LOS link.
 On dispersive channels, the optimum sampling point changes as the
severity of ISI changes.
Unequalized Performance
 For the LOS channel, the slot error probability Pse of DPIM is given by:
RP


1
avg 

P Q
 2 LM R 
se
b 

where R is the photodetector responsivity, η is the noise spectral density, Pavg is
the average transmitted optical signal power, Rb is the bit rate, M is bit resolution
and L = 2M.
.
 In a multipath channel,
the Pse is calculated by summing the error probabilities
in all possible sequences.
P
  P(b )
i i
se, DPIM
all i
where bi is the m-slot DPIM(NGB) sequence and
  y 

opt 
Q i
if a  1
 
i
0.5 

(
0
.
5
N
)
 
0


 
i
  
y 
  opt i 
Q
if a  0
 
i
0.5 

(
0
.
5
N
)
 
0

where opt is the optimum threshold
level, set to the midway value of RPave
(Tb)0.5
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Unequalized Power Penalty
 There is exponential growth in
power penalty with increasing
delay spread for all orders of
DPIM.
Average optical power penalty (dB)
5
4
L=4
L=8
L = 16
L = 32
 The average optical power
required to achieve a desirable
error performance is impractical
for normalized delay spread
grater than 0.1.
3
2
1
0
-2
10
-1
10
RMS delay spread / slot duration
0
10
 To mitigate the ISI the solution is
to incorporate an equalizer at
the receiver .
Equalization
 Maximum likelihood sequence detector : Though the optimum
solution, not suitable for variable symbol length modulation
schemes like DPIM since symbol boundaries are not known.
 Hence sub-optimum solutions based on finite impulse response
filters would be the preferred option.
 But equalization based on the finite impulse response (FIR) filter
suffers from severe performance degradation in time varying and
non-linear channels.1
 The equalization problem can be formulated as classification
problem and hence artificial neural network can be used to
reduce the effect of ISI.2,3
1- A. Hussain, J. J. Soraghan, and T. S. Durrani, IEEE Transactions on Communications, vol. 45, pp. 1358-1362, 1997.
2- J. C. Patra and N. R. N. Pal, Signal Processing, vol. 43, pp. 81 - 195, 1995.
3- L. Hanzo, C. H. Wong, and M. S. Yee, Adaptive wireless transceivers: Wiley-IEEE Press, 2002, pp. 299-383.
Equalization: A Classification Problem
 Classification capability of FIR filter equalizer is limited to a linear
decision boundary, which is a non-optimum classification strategy1.
 FIR base equalizers suffer from severe performance degradation in
time varying and non-linear channel2.
 The optimum strategy would be to have a nonlinear decision
boundary for classification.
 ANN is employed for equalization because of its capability to form
complex nonlinear decision regions.
- In fact both the linear and DFE are a class of ANN3 .
 Wavelet based equalization4.
1- L.Hanzo, et al, Adaptive wireless transceivers: Wiley-IEEE Press, 2002, pp. 299-383.
2- C. Ching-Haur, et al , Signal Processing,vol. 47, no. 2, pp. 145 - 158 1995.
3- S. Haykin, Communications Magazine, IEEE , vol.38, no.12, pp. 106-114, Dec. 2000
4- D. Cariolaro et al, IEEE Intern. Conf. on Communications, New York, NY, USA, pp. 74-78, 2000.
Block Diagram of Receiver Based on
Classification
Optical Optical
Signal Receiver
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Feature
Extraction
Pattern
Classification
Wavelet
Transform
Neural
Network
PostProcessing
For efficient classification, feature extraction tools are incorporated
in the receiver.
The receiver is made modular by having separate block for :
(a) Feature extraction (wavelet transform) and
(b) pattern classification (ANN).
WT-ANN based receiver outperforms the traditional equalizers1.
1- R. J. Dickenson and Z. Ghassemlooy, International Journal of Communications Systems, Vol. 18, No. 3, pp. 247-266, 2005.
Feature Extraction Tools
Time-Frequencies Mapping
Fourier
Transform
No timefrequency
Localization
Short-Time Fourier
Transform
Fixed time-frequency
resolution:
Uncertainty problem
Wavelet
Transform
No resolution
problem :Ultimate
Transform
CWT vs. DWT
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 Infinite scale in CWT, having highly redundant coefficients.
 Redundancy in CWT can be removed by utilizing the DWT.
 The DWT is easier to implement using filter bank of high
pass and low pass filters.
 Reduced computational time compared CWT.
 Possibility of denoising of signal by thresholding the wavelet
coefficient in DWT.
Discrete Wavelet Transform
Filtering
Signal
h[n]
Level 1 DWT
Down- coefficients
sampling
cD1
cD2
2
cA1
x[n]
g[n]
Level 2 DWT
coefficients
h[n]
2
cA2
2
g[n]
2
...
 DWT coefficient can efficiently be obtained by successive filtering and down
sampling.
 Signal is decomposed using high pass h[n] and a low pass g[n] filters and down
sampled by 2.
cD : yl [k ]   X ng[2k  n]
cA : yh [k ]   X nh[2k  n]
n
n
 The two filter are related to each other and are known as a quadrature mirror
filter.
Denoising Signal using DWT
 Denoising is performed by hard/soft thresholding of the detail
coefficients.
- Hard thresholding
-
0
(k ) HT  
1
if k  
if k  
Soft thresholding
(k ) ST  sgn(k )( k  )
- The threshold level  for universal threshold scheme :
  2 log N 
 :the variance of
the wavelet coefficient.
 Denoised signal
X d [n]  1 (( X [n])
where 1 is the inverse WT .
WT-ANN Based Receiver Model
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The receiver incorporates a feature extractor
(DWT) and a pattern classifier (ANN).
16-samples per bit.
Signal is decimated into W-bits discrete
sliding window. (i.e. each window contains a
total of 16W discrete samples ).
Information content of the window is changed
by one bit.
3-level DWT of each window is calculated.
DWT coefficients are denoised by:
a) Thresholding : A threshold is set and ‘soft’ or
‘hard’ thresholding are used for detail coefficients.
b) Discarding coefficients: detail coefficients are
completely discarded.
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Zj
Z(t)
Tb/n
Feature extractor
& pattern classifier
DWT
Zˆ j Threshold bˆ j
detector
ANN
Bit to decode
3 bit window
The denoised coefficient are fed to ANN.
ANN is trained to classify signal into two binary classed based on the DWT
coefficients.
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Simulation Parameters
Parameters
Data rate Rb
200 Mbps
Channel RMS delay spread Drms 1-10 ns
Value
No. of samples per bit
Mother wavelet
ANN type
No. of neural layers
No. of neurons in 1st layer
No. of neurons in 2nd layer
ANN activation function
ANN training algorithm
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Discrete Meyer
Feedforward back propagation
2
4
1
log-sigmoid, tan-sigmoid
Scaled conjugate gradient algorithm
ANN training sequence
Minimum error
Minimum gradient
DWT levels
100 symbols
1-30
1-30
3
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Simulation Flowchart
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Results
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Figure : The SER performance against the SNR for
unequalized, Linearly equalized and a DWT-ANN based
receiver at data rate of 200 Mbps for diffuse links with Drms
of 1, 5 and 10 ns.
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Unequalized DPIM- worst error
performance.
The unequalized error performance is not
practically acceptable for highly diffuse
channel like channels with Drms > 5ns.
Both linear and DWT-ANN equalizers show
improve error performance compared to
unequalized cases.
The DWT-ANN based receiver showed a
significant improvement in SER
performance compared to linear equalizer.
The SNR gain with DWT-ANN at the SER
of 10-5 is ~ 8.6 dB compared to linear
equalizer.
Performance of DWT-ANN also depends
on selection of mother wavelet, with
discrete Meyer wavelet showing the best
performance.
Further improvement in SER performance
can be achieved by using error control
coding.
Conclusions
 The traditional tool for signal detection and equalization is inadequate
in time-varying non-linear channel.
 Digital signal detection can be reformulated as feature extraction and
pattern classification.
 Both discrete and continuous wavelet transform is used for feature
extraction.
 ANN is trained for classify received signal into binary classes.
 DWT-ANN equalizers performance offers an SNR gain of almost 8 dB
at SER of 10-5 at data rate of 200 Mbps for all values of channel delay
spread.
 The rapid increase in the processing time of electronic devices can
make the system practically feasible.
 Practical implementation of the proposed system in the process
of being carried out at the photonics Lab, Northumbria University.
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Acknowledgement
 Northumbria University for supporting the research.
 OCRG and IML lab for providing require software for
simulation.
Thank you!
Questions/Suggestions/Comments