The Efficient Denoising Artificial Light
Interference using Discrete Wavelet Transform
with Application to Indoor
Optical Wireless System
S. Rajbhandari, Prof. Z. Ghassemlooy, Prof. M. Angelova
School of Computing, Engineering & Information Sciences,
University of Northumbria, Newcastle upon Tyne, UK.
sujan.rajbhandari@unn.ac.uk
http://soe.unn.ac.uk/ocr
Content
 Introduction to indoor optical wireless system (OWS)
 Challenges in OWS.

Artificial light interference, its effect in indoor OWS links and techniques to
mitigate.
 DWT based denoising.
 Realization of the propose system.
 Future works
 Conclusion
History of Optical Communication
 The very first form of wireless
speech communication was
achieved at optical
wavelengths in 1878 by
Alexander Graham Bell, more
than 25 years before Reginald
Fessenden did the same
thing with radio1.
Diagram of photophone from Bell paper 1
 Development of LASER in 60’s, optical fibre and semiconductor
has made the modern communication possible.
 The modern era of indoor wireless optical communications was
proposed in 1979 by F.R. Gfeller and U. Bapst 2. In fact it was the
first LAN proposed using any medium.
1
Alexander Graham BELL, American Journal of Sciences, Third Series, vol. XX, no.118, Oct. 1880, pp. 305- 324.
R. Gfeller and U. Bapst, Proceedings of the IEEE, vol. 67, pp. 1474- 1486, 1979.
2 F.
Optical Wireless System (OWS): Overview

Communication system using light
beams (visible and infrared)
propagated through the atmosphere
or space to carry information.
Typical optical wireless system components
 Optical transmitter
Light Emitting Diodes (LED)
Laser Diodes (LD)

Optical receiver
p-i-n Photodiodes.
Avalanche Photodiodes.
 Links
Line-of-sight(LOS)
Non-LOS
Hybrid
Optical wireless connectivity 1
1
M. Kavehrad, Scientific American Magazine, July 2007, pp. 82-87.
What OWS offers
 Abundance bandwidth  High data rate
 License free operation
 High Directivity  small cell size  can support multiple devices
within a room
 Free from electromagnetic interference  suitable for hospital
and library environment.
 cannot penetrate opaque surface like wall Spatial confinement
 Secure data transmission
 Compatible with optical fibre (last mile bottle neck?)
 Low cost of deployment
 Quick to deploy
 Small size, low cost component and low power consumptions.
 Simple transceiver design.
 No multipath fading
Challenges (Indoor)
Challenges
Causes
(Possible ) Solutions
Power limitation
Eye and skin safety.
Power efficient modulation
techniques, holographic diffuser,
transreceiver at 1500ns band
Noise
Intense ambient light
(artificial/ natural)
Optical and electrical band pass
filters, Error control codes
Intersymbol
interference (ISI)
Multipath propagation
(non-LOS links)
Equalization, Multi-Beam
Transmitter
No/Limited
mobility
Beam confined to small
area.
Wide angle optical transmitter ,
MIMO transceiver.
Shadowing
Blocking
LOS links
Diffuse links/ Cellular System/ wide
angle optical transmitter
Limited data rate
Large area photodetectors
Bandwidth-efficient modulation
techniques /Multiple small area photodetector.
Strict link set-up
LOS links
Diffuse links/ wide angle transmitter
Common Baseband Digital Modulation
Techniques
OOK
 Simple to implement
 High average power requirement
 Suitable for Bit Rate greater than 30Mb/s
 Performance detiorates at higher bit rates
PPM
 Complex to implement
 Lower average power requirement
 Higher transmission bandwidth
 Requires symbol and slot synchronisation
DPIM
 Higher average power requirement
compared with PPM
 Higher throughput
 Built in symbol synchronisation
 Performance midway between PPM and
OOK.
DH-PIM





The highest symbol throughput
Lower transmission bandwidth than PPM and DPIM
Built in symbol synchronisation
Higher average power requirement compared with PPM and DPIM.
Complex decoder
Artificial Light Interference (ALI)
Normalised power/unit wavelength
Pave)amb-light >> Pave)signal (Typically 30 dB with no optical filtering)
1.2
Sun
1
Incandescent
2nd window IR
0.8
1st window IR
0.6
 Spectral overlapping of signal
and interference produce by
fluorescent lamp driven by
electronic ballasts
Fluorescent
0.4
x 10
0.2
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0
 Dominant noise source at low
data rate.
 can cause serious performance
degradation as the interference
amplitude can be much higher
than signal amplitude.
Wavelength (m)
Optical power spectra of common ambient infrared sources. Spectra
have been scaled to have the same maximum value.
 The effect of noise is minimised
using combination of the optical
band pass filter and electrical low
pass filter.
Fluorescent Light Interference Model1
 mhigh(t)  high frequency component.
 mlow(t) low frequency component.
Low frequency component
Optical power penalty due to FLI
High frequency component
A. J. C. Moreira, R. T. Valadas, and A. M. d. O. Duarte, IEE Proceedings -Optoelectronics, vol. 143, pp. 339-346.
ALI-Possible Solutions






1 J.
Differential receiver1
Differential optical filtering2
Electrical high pass filter3,4
Polarisers 5
Angle diversity receiver 6,7
Discrete wavelet transform based denoising8,9
R. Barry, PhD Dissertation, University of California at Berkeley, 1992
A.J.C Moreira, R. T. Valadas, A. M. De Oliveira Duarte, Optical Free Space Communication Links, IEE Colloquium on ,
vol., no., pp.5/1-510, 19 Feb 1996.
3 R. Narasimhan, M. D. Audeh, and J. M. Kahn, IEE Proceedings - Optoelectronics, vol. 143, pp. 347-354, 1996.
4 A. R. Hayes, Z. Ghassemlooy , N. L. Seed, and R. McLaughlin, IEE Proceedings - Optoelectronics vol. 147, pp. 295300, 2000.
5S. Lee, Microwave and Optical Technology Letters, vol. 40, pp. 228-230, 2004.
6R. T. Valadas, A. M. R. Tavares, and A. M. Duarte, International Journal of Wireless Information Networks, vol. 4, pp.
275-288, 1997 .
7J. M. Kahn, P. Djahani, A. G. Weisbin, K. T. Beh, A. P. Tang, and R. You, IEEE Communications Magazine, vol. 36, pp.
88-94, 1998.
8 S. Rajbhandari; Z. Ghassemlooy; and M. Angelova, IJEEE, Vol. 5, no. 2 ,pp102-111. 2009.
9 S. Rajbhandari; Z. Ghassemlooy; and M. Angelova, Journal of Lightwave Technology, on print.
2
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
Discrete Wavelet Transform
(14)
Filtering
Signal
h[n]
Level 1 DWT
Down- coefficients
sampling
y1h
y2h
2
h[n]
y1l
x[n]
g[n]
Level 2 DWT
coefficients
2
y2l
2
g[n]
2
 Coefficient can efficiently be obtained by successive filtering and down sampling.
cD : y [k ]   X ng[2k  n]
cA : yl [k ]   xnh[2k  n]
h
n
n
 The two filter are related to each other and are known as a quadrature mirror
filter.
 Reconstruction is inversion of decomposition process  filter, up sample and
combine.
DWT based Denoising
 DWT is a multiresolutional analysis (MRA) tool 
signals are divided into half-frequency bands at each
level of the decomposition.
 Separate the received signal into different frequency
bands.
 Remove the frequency band that corresponding to
interference.
Reconstruct the signal using inverse DWT.
 Challenge: spectral overlap between the signal and
interference (both signals have high PSD at a low
frequency region).
 The denoising should be carried out to ensure that
information lost is minimum.
Multiresolutional analysis tree
System Descriptions
Input
s(t)
bits di Transmitter
filter
p(t)
x(t)
2Pavg
Multipath
channel
h(t)
v(t)
mfl(t)
n(t)
z(t)
Output
slots d̂
Matched y(t) y(n)
 t 
Wavelet
filter
Denoising
r(t)
sample
i
R
DWT
Processing
 FLI is a low frequency band signal, the approximation
coefficients need to be manipulated.
 For denoising proposes, the approximation coefficients
corresponding to the FLI are made equal to zero so that
reconstructed signal is free from FLI.
 The signal is then reconstructed using the inverse DWT .
IDWT
DWT based Denoising
 Complete closer of eye in the eyediagram of the signal corrupted by
ALI  high BER.

Wide opening of the eye with
wavelet denoising.
 The number of decomposition level
for DWT is calculated using:
Received OOK signal in the presence of the FL
interference,
k   log 2 Ts  0.5E 6
where is the  x ceiling function
 Approximate cut-off frequency of 0.5
MHz is used as it provide near
optimum performance.
The eye-diagram of
received
signal
corrupted by ALI
The eye diagram of
received signal with
wavelet denoising.
DWT based Denoising
PSD of the OOK with FLI and DWT
denoising at 2 Mbps
PSD of the OOK with FLI and DWT
denoising at 200 Mbps
 No significant changes in PSD at frequency > 0.5 MHz.

Significant portion of the spectral content at < 0.3 MHz is removed with no DC
contents.
 Spectral overlap between signal and interference  power penalty.
Performance of OOK with DWT
 DWT based receiver reduces the
optical power requirement significantly.
Above data rate of 40 Mbps, the
optical power penalty for OOK-NRZ
is less than 1.5 dB.
 Optical power penalty is the highest
for OOK due to a high DC content.
 Optical power penalty for PPM and
DPIM is ~0.5 dB.
 Since the PPM has zero spectral
component near DC value, PPM offers
improved performance.
The normalized OPP to achieve a error rate of
10-6 for OOK, 8-PPM and 8-DPIM for ideal and
interfering channels and with DWT denoising at
data rates of 10 - 200 Mbps.
DWT vs. HPF
HPF
8
7
7
6
5
4
3
2
1
0
5
6
7
8
9
Decomposition level
20 Mbps
50 Mbps
100 Mbps
200 Mbps
10
11
Optical power penalty (dB)
Optical power penalty (dB)
DWT
8
20 Mbps
50 Mbps
100 Mbps
200 Mbps
6
5
4
3
2
1
0
0
0.5
1
Cut-off frequency (MHz)
1.5
2
Performance
Displays similar or better performance
compared to the best achieved with the HPF.
Significantly inferior performance at high
data rate compared to DWT.
Optimization
Optimization is not necessary as
decomposition level can only be positive
integer.
Optimization is necessary to obtain best
performance.
Complexity
Reduced complexity compared to HPF.
Example, the maximum number operation for
‘db8’ wavelet is 60n, n length of input signal.
Realization
Easy as repetitive structure is used.
High.
Example, for a HPF of order L, the total number
of floating point operations is nL/2. L=2148 at
data rate of 200 Mbps .
Realization becomes difficult with increasing in
order.
Implementation- DWT
Implementation- TI DSP
DSP Board
Using TI DMS320C6713 DSP board + Matlab/Simulink
Conclusion
 Indoor optical wireless systems will have a major role in future indoor personal
communication.
 A number of key challenges needs to be address before fully potential can be
realized.
 Artificial light interference is a dominant noise source that impair the link
performance.
 Artificial light interference can be reduced effectively by using the discrete
wavelet transform.
 Discrete wavelet transform provide improved performance with reduced
complexity compared to the high pass filter.
 Discrete wavelet transform based denoising can easily be realized using DSP.
Acknowledgement
 Northumbria university for providing an studentship.
 My supervisors: Prof. Maia Angelova and Prof. Fary
Ghassemlooy.
 All my colleagues.
 Finally my family members.