1. Z. Ghassemlooy & S Rajbhandari
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

2. 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.

4. Power Spectra of Ambient Light Sources
Wavelength (m)
Normalised power/unit wavelength
0.2
0.4
0.6
0.8 1
1.2
0.3
0.5
0.7
0.9
1.0
1.1
1.3
1.4
1.5
Sun
Incandescent
x 10
1st window IR
Fluorescent
Pave)amb-light >> Pave)signal (Typically 30 dB with no optical filtering)
2nd window IR

5. Classification of Indoor OW LinksRX TX
(Diffuse)
Directed
Hybrid
Non-directed
Line-of-sight
Non-line-of-sight

6. Indoor OWC - Challenges
Causes
(Possible ) Solutions
Power limitation
Eye and skin safety.
Power efficient modulation techniques/holographic diffuser/ Transreceiver at 1500 nm 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 photo-detectors
Bandwidth-efficient modulation techniques/Multiple small area photo-detector
Strict link set-up
LOS links
Diffuse links/ wide angle transmitter

7. Modulation Techniques

8. Normalized Power and Bandwidth Requirement2 3 4 5 6 7 8 10 12 14 16 18 20
Bit resolution, M
Normalized bandwidth requirement
PPM
DH-PIM1
DPIM
DH-PIM2
OOK
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 2 3 4 5 6 7 8
-16
-14
-12
-10 -8 -6 -4 -2
Bit Resolution, M
Normalized Power Requirement (dB)
DH-PIM2
PPM
DH-PIM1
DPIM

9. Power Spectral Density
Notice the DC component:-
when filtered will result in base
line wander effect

10. 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
Fast Multireceiver Channel Estimation
iterative site-based method for estimating the impulse response of optical wireless channels.
simultaneous evaluation of channels for a number of receiver or transmitter locations,
Offers significantly improved calculation times

11. Channel Model - Ceiling Bounce Model
Developed by Carruthers and Kahn.
Impulse response is:
LOS
Diffuse
Diffuse shadowed
LOS shadowed
where u(t) is the unit step function and a is related to the RMS delay spread D

12. 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 Rx Tx

13. Received signal for non-LOS Links
OWC - Diffuse Links
Different paths ─>Different path lengths ─> different delay ─>ISI.
ISI ─> Delay Spread Drms ─> Room design and size
Impulse response of channel
Problems:
High path loss
Limited data rate due to ISI
Power penalty due to ISI Rx Tx 2 4 6 8 10
-0.4
-0.2
0.2
0.4
0.6
0.8 1
1.2
Normalized Time
Amplitude
Received signal for non-LOS Links

14. 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. O’Brien
Equalisation
Diversity: S. H. Khoo et al 2001
Wavelet and AI based equalisers: Z. Ghassemlooy et al
Some investigative work and preliminary simulations on MLSD undertaken but not pursued. MLSD can be implemented after any compensation technique.

15. 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

16. 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

17. ANN Output Neurons Hidden layer Input 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)
Hidden
layer
Input
Neurons
Output

18. ANN Activation Function f(.) Sigmoid function -x1
Inputs w1
x1w1
Weights Z xn
xnwn wn ∑
f(.)
Activation
function
Output
Bias bi
Activation Function f(.)
Sigmoid function -
Linear function if , if if
Any function that is differentiable

19. 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.

20. 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.
Error signal en in
Neural
network on
Comparator tn
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

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

22. OWC Link A feedforward back propagation ANN
PPM
Encoder
h(t) ∑
Neural
Network
Decision
Device
Optical
Transmitter
Receiver
n(t)
Decoder
X(t)
Matched
Filter Zj
Zj-1 .
Zj-n Yj
Z(t) M
Ts = M/LRb Xj
A feedforward back propagation ANN
ANN is trained using a training sequence at the operating SNR
Trained AAN is used for equalization

23. 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

24. Simulation Flow Chart

25. Simulation Parameters
Values
Number of layers 2
Number of neurons in each layer
36,1
Activation function
tan-sigmoid,
log-sigmoid
Training algorithm
scaled conjugate gradient algorithm
Minimum error
1-30
Minimum gradient

26. Simulation Parameters – Contd.
Values
OOK
PPM
DPIM
Data rate, Rb (Mbps)
150
Bit resolution, M 3
Slot duration, Ts
1/ Rb
M/( Rb .2M)
2M/(2M+1) Rb
Training sequence
2000 bits
300 symbols
600 symbols
RMS delay spread, Drms(ns) 10 5 2
Normalized time delay (Drms/Ts)
1.5
0.75
0.3 4
2.3
1.13
0.45
Delayed samples 8 22 11 6 13 7

27. Results and Discussion
Error performance for LOS links (150 Mbps) 2 4 6 8 10 12 14 -6 -5 -4 -3 -2 -1
SNR( dB)
SER
8-PPM
8-DPIM
OOK
PPM requires the least SNR to achieve a desirable slot error rate (SER)
OOK shows the highest power requirement to achieve a desirable SER

28. 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 5 10 15 20 25 30 35 40 -6 -5 -4 -3 -2 -1
SNR( dB)
SER
LOS
PPM
DPIM
OOK
Unequalized PPM
Unequalized DPIM
Unequalized OOK

29. Results and Discussion
OOK performance (Rb = 150Mbps, Drms = 5ns) 5 10 15 20 25 30 35 40 -6 -5 -4 -3 -2 -1
SNR( dB)
SER
LOS
Unequalized
ANN equalizer
Linear equalizer
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

30. 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 5 10 15 20 -6 -5 -4 -3 -2 -1
SNR( dB)
SER
DPIM
PPM
OOK
ANN Equalized
LOS

31. Results and Discussion
ANN Equalizer (Rb = 150Mbps, Drms = 1, 2, &10 ns) 5 10 15 20 25 -6 -5 -4 -3 -2 -1
SNR(dB)
SER
DPIM
PPM
10ns
2ns
1ns
OOK
Equalized PPM shows the best performance in less dispersive channel (Drms<2)
Equalized DPIM shows the best SER performance in highly dispersive channel (Drms >2)

32. Wavelet-AI Receiver Signal decimated into 3 bit sliding windows.
Transmitter
filter g(t)
Diffuse IR channel h(t)
ADC
Slicer
Input
bits
Output
XPavg X R
Artificial Intelligence
Anti-
alias
(LPF)
noise
n(t) +
Wavelet Analysis
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.
Describe the general Wavelet-Ai receiver process and the need for training or adaptive learning.

33. Signal Sample ‘The Window’
3 bit sliding window 1 2 8 3
For OOK signal decimated into 3 bit windows.
Each window is processed into wavelet coefficients by the continuous wavelet transform (CWT).
Introduce data window.
Discuss its modification for the PPM binary symbol detector

34. Simulation Results - Multipath Propagation 3
Compare performances with equalised cases.
Again briefly discuss Wavelet AI Binary Symbol Detector.
Close by pointing out similar or better performance of Wlt-AI and its flexibility when implemented as the basis of software receiver.
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

35. 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.

36. 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.

37. Thank you!