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					     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.

 Introduction to indoor optical wireless system (OWS)

 Challenges in OWS.

   Artificial light interference, its effect in indoor OWS links and techniques to

 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.
  2 F.  R. Gfeller and U. Bapst, Proceedings of the IEEE, vol. 67, pp. 1474- 1486, 1979.
              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
  Non-LOS                                               Optical wireless connectivity 1
  Hybrid                          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    Optical and electrical band pass
                     (artificial/ natural)    filters, Error control codes
Intersymbol          Multipath propagation    Equalization, Multi-Beam
interference (ISI)   (non-LOS links)          Transmitter
No/Limited           Beam confined to small   Wide angle optical transmitter ,
mobility             area.                    MIMO transceiver.
Shadowing            LOS links                Diffuse links/ Cellular System/ wide
Blocking                                      angle optical transmitter
Limited data rate    Large area photo-        Bandwidth-efficient modulation
                     detectors                techniques /Multiple small area photo-
Strict link set-up   LOS links                Diffuse links/ wide angle transmitter
                 Common Baseband Digital Modulation
 Simple to implement
 High average power requirement
 Suitable for Bit Rate greater than 30Mb/s
 Performance detiorates at higher bit rates
 Complex to implement
 Lower average power requirement
 Higher transmission bandwidth
 Requires symbol and slot synchronisation

 Higher average power requirement
 compared with PPM
 Higher throughput
 Built in symbol synchronisation
 Performance midway between PPM and

   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)

                                                                                                                            Dominant noise source at low
                   Pave)amb-light >> Pave)signal (Typically 30 dB with no optical filtering)                                 data rate.
Normalised power/unit wavelength

                                    1                Sun                        Incandescent
                                                                                                       2nd window IR
                                                                                                                            Spectral overlapping of signal
                                                                                                                             and interference produce by
                                                                                                                             fluorescent lamp driven by
                                                                           1st window IR
                                   0.6                                                                                       electronic ballasts
                                                                                                                            can cause serious performance
                                                                               x 10
                                   0.2                                                                                       degradation as the interference
                                                                                                                             amplitude can be much higher
                                                                                                                             than signal amplitude.









                                                                    Wavelength (m)
Optical power spectra of common ambient infrared sources. Spectra                                                           The effect of noise is minimised
have been scaled to have the same maximum value.
                                                                                                                             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

       Differential receiver1
       Differential optical filtering2
       Electrical high pass filter3,4
       Polarisers 5
       Angle diversity receiver 6,7
       Discrete wavelet transform based denoising8,9
1 J. R. Barry, PhD Dissertation, University of California at Berkeley, 1992
2  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. 295-

300, 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.
               Feature Extraction Tools

               Time-Frequencies Mapping

  Fourier         Short-Time Fourier          Wavelet
 Transform           Transform               Transform

  No time-       Fixed time-frequency       No resolution
 frequency            resolution:         problem :Ultimate
Localization     Uncertainty problem         Transform
                           Discrete Wavelet Transform

                                    Level 1 DWT                     Level 2 DWT
                             Down- coefficients
               Filtering    sampling
  Signal        h[n]          2
                                                    h[n]        2
 x[n]                                 y1l
                g[n]          2
                                                    g[n]        2

 Coefficient can efficiently be obtained by successive filtering and down sampling.
        cD : y [k ]   X ng[2k  n]
              h                        cA : yl [k ]   xnh[2k  n]
                   n                            n

 The two filter are related to each other and are known as a quadrature mirror
 Reconstruction is inversion of decomposition process  filter, up sample and
                         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

 Remove the frequency band that corresponding to
                                                          Multiresolutional analysis tree
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.
                     System Descriptions

Input                                                                                                                               Output
bits di Transmitter
                    s(t)           x(t)   Multipath                   v(t)       z(t)
                                                                                        Matched y(t) y(n)            t                 ˆ
                                                                                                                                    slots d
           filter                         channel                                        filter            Wavelet                        i
            p(t)                            h(t)                                          r(t)            Denoising

                                                      mfl(t)   n(t)          R
                                                                                                 DWT        Processing       IDWT

     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 .
                              DWT based Denoising

 Complete closer of eye in the eye-
  diagram of the signal corrupted by
  ALI  high BER.

    Wide opening of the eye with
     wavelet denoising.
                                            Received OOK signal in the presence of the FL
 The number of decomposition level         interference,
  for DWT is calculated using:
     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          The eye diagram of
                                          received         signal     received signal with
                                          corrupted by ALI            wavelet denoising.
                                   DWT based Denoising

      PSD of the OOK with FLI and DWT            PSD of the OOK with FLI and DWT
             denoising at 2 Mbps                      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
 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.
                                            The normalized OPP to achieve a error rate of
                                            10-6 for OOK, 8-PPM and 8-DPIM for ideal and
 Since the PPM has zero spectral           interfering channels and with DWT denoising at
 component near DC value, PPM offers        data rates of 10 - 200 Mbps.
 improved performance.
                                                                                        DWT vs. HPF

                                                            DWT                                                                                        HPF
                                                   8                                                                                      8
                                                                                                                                                                                     20 Mbps
                                                   7                                                                                      7                                          50 Mbps
                                                                                                                                                                                     100 Mbps
                                                                                                                                                                                     200 Mbps

                                                                                                             Optical power penalty (dB)
                      Optical power penalty (dB)
                                                   6                                                                                      6

                                                   5                                                                                      5

                                                   4                                                                                      4

                                                   3                                                                                      3

                                                   2                                                                                      2
                                                                                         20 Mbps
                                                                                         50 Mbps
                                                   1                                                                                      1
                                                                                         100 Mbps
                                                                                         200 Mbps
                                                    5   6   7          8            9   10        11                                      0
                                                                                                                                           0   0.5               1             1.5              2
                                                                Decomposition level
                                                                                                                                                     Cut-off frequency (MHz)
Performance    Displays similar or better performance                                                  Significantly inferior performance at high
               compared to the best achieved with the HPF.                                             data rate compared to DWT.
Optimization   Optimization is not necessary as                                                        Optimization is necessary to obtain best
               decomposition level can only be positive                                                performance.
Complexity     Reduced complexity compared to HPF.                                                     High.
               Example, the maximum number operation for                                               Example, for a HPF of order L, the total number
               ‘db8’ wavelet is 60n, n length of input signal.                                         of floating point operations is nL/2. L=2148 at
                                                                                                       data rate of 200 Mbps .
Realization    Easy as repetitive structure is used.                                                   Realization becomes difficult with increasing in
Implementation- DWT
            Implementation- TI DSP

                              DSP Board

Using TI DMS320C6713 DSP board + Matlab/Simulink

 Indoor optical wireless systems will have a major role in future indoor personal

 A number of key challenges needs to be address before fully potential can be

 Artificial light interference is a dominant noise source that impair the link

 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.

 Northumbria university for providing an studentship.

 My supervisors: Prof. Maia Angelova and Prof. Fary

 All my colleagues.

 Finally my family members.