Performance of Diffuse Indoor Optical Wireless Links Employing Neural and Adaptive Linear Equalizers

1
Performance of Diffuse Indoor Optical Wireless
Links Employing Neural and Adaptive Linear
Equalizers

• Z. Ghassemlooy S Rajbhandari
• Optical Communications Research Group, School of
  Computing, Engineering Information Sciences,
  University of Northumbria, 
• Newcastle upon Tyne, UK
• ICICS 2007 Singapore

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Outline

• Optical wireless introduction
• Mutipath induces ISI
• ANN based equalizer
• Wavelet-ANN receiver
• Final comments

3
Optical Wireless Communication What Does It
Offer?

• Abundance bandwidth
• No multipath fading
• High data rates
• Protocol transparent
• Secure data transmission
• License free
• Free from electromagnetic interference
• Compatible with optical fibre (last mile bottle
  neck?)
• Low cost of deployment
• Easy to deploy
• Etc.

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Power Spectra of Ambient Light Sources
Pave)amb-light gtgt Pave)signal (Typically 30 dB
with no optical filtering)
2nd window IR
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Classification of Indoor OW Links
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Indoor OWC - Challenges
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Modulation Techniques
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Normalized Power and Bandwidth Requirement

• PPM the most power efficient
• while requires the largest
• bandwidth
• DH-PIM2 is the most bandwidth
• efficient
• DH-PIM and DPIM shows almost identical bandwidth
  requirement and power requirement
• There is always a trade-off
• between power and bandwidth

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Power Spectral Density
Notice the DC component- when filtered will
result in base line wander effect
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Optical Wireless - Channel Model

• Basic system models F. R. Gfeller et al 1979,
  J. M. Kahn et al 1995,
• Measurement studies - H. Hashemi et al 1994, J.
  M. Kahn et al 1995,
• - Diffuse shadowing
• Statistical models - J.B. Carruthers et al 1997
• Ray tracing techniques (to obtain simulated
  channel responses) - J.R. Barry, J.R., et al.
  1995, F.J. Lopez-Hernandez, et al, 2000
• Segmentation of reflecting surfaces ray tracing
  techniques to calculate the intensity and
  temporal distributions - S. H. Khoo et al 2001
• Fast multi-receiver channel estimation - J.B.
  Carruthers et al 2002

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Channel Model - Ceiling Bounce Model

• Developed by Carruthers and Kahn.
• Impulse response is

where u(t) is the unit step function and a is
related to the RMS delay spread D
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OWC - LOS Links

• Least path loss
• No multipath propagation
• High data rates
• Problems
• Noise is limiting factor
• Possibility of blocking/shadowing
• Tracking necessary
• No/limited mobility

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OWC - Diffuse Links

• Different paths -gtDifferent path lengths -gt
  different delay -gtISI.
• ISI -gt Delay Spread Drms -gt Room design and size
• Impulse response of channel
• Problems
• High path loss
• Limited data rate due to ISI
• Power penalty due to ISI

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How to Combat Noise and Dispersion?

• Noise Filtering Optical or Electrical
• Match Filtering
• Maximises signal-to-noise ratio,
• Modulation Z. Ghassemlooy et al
• Coding Block codes, Convolutional and Turbo
  codes.
• Spread Spectrum
• Tracking Transmitters D. Wisely et al
• Imaging Receivers J.M. Kahn et al
• Integrated Optical Wireless Transceivers D.C.
  OBrien
• Equalisation
• Diversity S. H. Khoo et al 2001
• Wavelet and AI based equalisers Z. Ghassemlooy
  et al

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Techniques to Mitigate the ISI

• Optimal solution - Maximum likelihood sequence
  detection.
• - Issues complexity and delay
• Sub-optimal solution - Linear or decision
  feedback equalizer based on the finite impulse
  response (FIR) digital filter
• - The impulse response of filter c(f) 1/h(f),
  where h(f) is the frequency response of channel

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FIR Filter Equalizer(Classical Signal Processing
Tool)

• Assumptions
• The statistics of noise is known (normally assume
  to be Gaussian)
• The channel is stationary or quasi-stationary
• The channel characteristics are known (at least
  partially)
• Signals are linear
• Problems Non-linearity, time-varying and
  non-Gaussianity of real signals and channel
• Solution Artificial neural network (ANN) based
  signal processing which takes into account
  non-linearity, time-varying and non-Gaussianity
  of signal and channel

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ANN

• One or more hidden layer(s)
• Output is function of sum and product of many
  functions
• Useful tool because of learning and adaptability
  capabilities
• Extensively used as a classifier
• Application in many areas like engineering,
  medicine, financial, physics and so on
• Training is necessary to adjust the free
  parameters ( weight) before can be used as
  classifier
• Supervised and unsupervised learning (training)

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ANN

• Activation Function f(.)
• Sigmoid function -
• Linear function - if ,
  if
• if
• Any function that is differentiable

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ANN

• Both the multilayer perceptrons (MLP) and the
  radial basic function (RBF) have been used for
  equalization
• RBF requires a larger number of hidden nodes at
  lower values of SNR
• The cascaded MLP and RBF outperform both the MLP
  and RBF in terms of the BER performance

• Learning rules for MLP
• The error-correction wij are renewed after
  each iteration
• - the most simplest
• The Boltzmann
• Hebbian
• Whichever training rule is used, the basic
  principle is to modify wij so that the error
  function is decreased after each iteration.

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ANN Supervised Learning (Training)
Target to minimize the error en between target
vector set tn and neural network output on for
all input vector set in.

• Algorithms
• Compare tn and on to determine en ( tn-on)
• Adjust wn and bi to reduce the error en
• Continue the process until en is small

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OWC System Block Diagram
Input data X(t)
Output data
Equalizer
Tx
Threshold detector
h(t)
Rx
?
n(t)
Adaptive Linear Equalizer
ANN Equalizer
For a non-stationary environment
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OWC Link

• A feedforward back propagation ANN
• ANN is trained using a training sequence at the
  operating SNR
• Trained AAN is used for equalization

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ANN Training Process

• The channel is time-varying
• To estimate channel parameters, a training
  sequence is transmitted at regular interval for
  tracking changes in the channel
• The information on channel is stored in the form
  of weights that are updated on receiving the
  training sequence
• The signal flows from input to the output
  (feedforward) while the error signal propagates
  backward, hence the name feedforward
  backpropagation NN
• The learning duration and the number of iteration
  required to adjust the NN parameters depends on
  the complexity of learning task
• Here the aim is not to optimize the learning task
  but to send a learning sequence of certain length
  to allow the NN to estimate new channel parameters

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Simulation Flow Chart
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Simulation Parameters
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Simulation Parameters Contd.
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Results and Discussion
Error performance for LOS links (150 Mbps)

• PPM requires the least SNR to achieve a
  desirable slot error rate (SER)
• OOK shows the highest power requirement to
  achieve a desirable SER

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Results and Discussion
Unequalized (Rb 150Mbps, Drms 5ns)

• Unequalized OOK requires
• 27dB more SNR compared to LOS link at SER
  of 10-5
• For high values of normalized delay spread
  increasing the optical power will not improve
  error performance
• PPM suffers the most severely in a diffuse
  link because of the short pulse duration

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Results and Discussion
OOK performance (Rb 150Mbps, Drms 5ns)

• ANN equalizer and linear
• equalizer shows identical
• performance
• Power penalty is 6.6 dB compared to LOS links
  at SER of 10-5
• SNR gain is 20 dB compared to unequalized
  performance at SER of
• 10-5

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Results and Discussion
ANN Equalizer (Rb 150Mbps, Drms 5ns)

• Performance of equalized DPIM
• and PPM is better than OOK even in highly
  dispersive channel
• DPIM show the best SER performance.
• Power penalty is 14.3dB, 9.2dB, 6.7dB for
  equalized PPM, DPIM and OOK compared to
  corresponding LOS performance for a SER of 10-5 .

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Results and Discussion
ANN Equalizer (Rb 150Mbps, Drms 1, 2, 10 ns)

• Equalized PPM shows the best performance in less
  dispersive channel (Drmslt2)
• Equalized DPIM shows the best SER performance in
  highly dispersive channel (Drms gt2)

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Wavelet-AI Receiver

• Signal decimated into 3 bit sliding windows.
• Each window is transformed into wavelet
  coefficients by the CWT process.
• The coefficients are passed to the neural network
  for classification.

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Signal Sample The Window

• For OOK signal decimated into 3 bit windows.
• Each window is processed into wavelet
  coefficients by the continuous wavelet transform
  (CWT).

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Simulation Results -Multipath Propagation 3
Equalised traditional receiver architecture
Wlt-AI reference (OOK RZ)
Equalised traditional receiver architecture
Wlt-AI reference (PPM)
Normalised to 2.5Mb/s for BER 10-6 OOK RZ
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Conclusions

• Artificial neural network as an equalizer shows
  similar error performance to the linear equalizer
• Equalized PPM shows the best performance in less
  dispersive channel while DPIM shows the best
  error performance in highly dispersive channel
• Power penalty for equalized OOK is 11.5 dB in
  highly dispersive channel (Drms 10 ns) at high
  data rate of 150Mbps making it feasible for
  practical implementation.

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Issues and Future Works

• Higher sampling rate (at least 8 samples per
  bit)
• Hardware complexity
• The need for parallel processing, at the
  moment
• Adaptive error control decoding using neural
  
• network.
• Combine equalization and decoding as a single
  
• classification problem
• Wavelet network for equalization and decoding
• Development of high performance pointing,
  acquisition,
• and tracking.

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Thank you!