Performance of Diffuse Indoor Optical Wireless Links Employing Neural and Adaptive Linear Equalizers...
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Transcript of Performance of Diffuse Indoor Optical Wireless Links Employing Neural and Adaptive Linear Equalizers...
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
Outline
Optical wireless – introduction Mutipath induces ISI ANN based equalizer Wavelet-ANN receiver Final comments
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.
Power Spectra of Ambient Light Sources
Wavelength (m)
No
rma
lise
d p
ow
er/u
nit
wa
vele
ng
th
0
0.2
0.4
0.6
0.8
1
1.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
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
Classification of Indoor OW Links
RX
TX TX
RX
(Diffuse)
TX
RX
Directed Hybrid Non-directed
Line-of-sight
Non-line-of-sight
TX TX RXTX RX
RX
Indoor OWC - Challenges
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
Modulation Techniques
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
2 3 4 5 6 7 80
2
4
6
8
10
12
14
16
18
20
Bit resolution, M
Nor
mal
ized
ban
dwid
th r
equi
rem
ent
PPM
DH-PIM 1
DPIM
DH-PIM 2
OOK
2 3 4 5 6 7 8-16
-14
-12
-10
-8
-6
-4
-2
0
Bit Resolution, M
Nor
mal
ized
Pow
er R
equi
rem
ent (
dB)
DH-PIM2
PPM
DH-PIM1
DPIM
Power Spectral Density
0 1 2 3 4 5 6 0
1
2
3
4
5
6
8-PPM
OOK
32-DPIM
16-DPIM
8-DPIM
Normalised frequency (f/Rb)
P S D
0 1 2 3 4 5 6 0
1
2
3
4
5
6
8-PPM
OOK
32-DPIM
16-DPIM
8-DPIM
Normalised frequency (f/Rb)
P S D
Notice the DC component:- when filtered will result in base line wander effect
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
Channel Model - Ceiling Bounce Model
Developed by Carruthers and Kahn.
Impulse response is:
)(
6),(
7
6
tuat
aath
11
13
12
aD
where u(t) is the unit step function and a is related to the RMS delay spread D
LOS
Diffuse
Diffuse shadowed
LOS shadowed
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
RxRx
TxTx
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
RxRxTxTx
0 2 4 6 8 10-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Normalized Time
Am
plitu
de
Received signal for non-LOS Links
)(7
61.06
)(1.0
tut
rmsD
th
rmsD
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
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
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
ANN
Hiddenlayer
Input
Neurons
Output 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)
ANN
Activation Function f(.)• Sigmoid function -
• Linear function - if , if if • Any function that is differentiabledifferentiable
)exp(1/1)( iZi
Zf
)exp(1/1)( iZi
Zf
1)( iZf
1)( iZf 5.0iZiZ
iZf )( 5.05.0 iZ
0)( iZf 5.0iZ
x1
Inpu
ts
w1
x1w1
Weights
)(zfy Z
n
iii xwz
1
xn
xnwn
wn
∑ f(.)
Activation function
Output
Bias bi
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.
Error signal en
in Neural network
on Comparator tn
ANN Supervised Learning (Training)
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
Target: to minimize the error en between target vector set tn and neural network output on for all input vector set in.
OWC System Block Diagram
Tx
InputdataX(t)
h(t)
n(t)
∑ Rx
ANNEqualizer
ANNEqualizer
AdaptiveLinear
Equalizer
AdaptiveLinear
Equalizer
Equalizer Thresholddetector
Outputdata
For a non-stationary environment
OWC Link
A feedforward back propagation ANN ANN is trained using a training sequence at the operating SNR Trained AAN is used for equalization
PPMEncoder
h(t)∑
NeuralNetwork
DecisionDevice
OpticalTransmitter
Optical
Receiver
n(t)
PPMDecoder
X(t)
MatchedFilter
ZjZj
Zj-1
.
Zj-n
.
Yj
Z(t)
M
0 0 1 0Ts = M/LRb
Xj
M0 1 0 0
)()()()( tnthtXtZ
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
Simulation Flow Chart
S tar t
G en er a te O O KR Z d a ta s tr eam
G en er a tem u ltip a th h ( t)
C o n v o lv e d a tas tr eam & h ( t)
Ad d tr a in in gAW G N
W in d o w th ed ata s tr eam
3 s e ts
T r a in n eu r a ln e tw o r k
G en er a te O O KR Z d a ta s tr eam
G en er a tem u ltip a th h ( t)
C o n v o lv e d a tas tr eam & h ( t)
Ad d s im u la tio nAW G N
W in d o w th ed ata s tr eam
C las s if y u s in gn eu r a l n e tw o r k
T h r es h o ldn etw o r k o u tp u t
S a v eRe s u lt
C alc u la te BE R
L o o p ?
YesN o
BE R ? D ec r eas eAW G N Valu e
T r a in ?
P lo t r es u lts
I n f o r m u s ers im ' en d
E n d
3 d a ta s e ts a r e r eq u ir ed eac h tim eth e n e tw o r k is tr a in ed .
I f th e n e tw o r k is to b e tr a in ed w ithan o th er n o is e f ig u r e , s ta r t ag a in .
I s th e BE R ta r g e t m et?
Yes N o
Yes
N o
Yes
N o
1 0 b lo c k s o fd a ta a r ep r o c es s edb ef o r e lo o pex its .
T r a in D etec t
Simulation Parameters
Parameters ValuesNumber of layers 2
Number of neurons in each layer 36,1
Activation functiontan-sigmoid,log-sigmoid
Training algorithmscaled conjugate gradient
algorithm
Minimum error 1-30
Minimum gradient 1-30
Simulation Parameters – Contd.
Parameters ValuesOOK PPM DPIM
Data rate, Rb (Mbps) 150 150 150
Bit resolution, M 3 3
Slot duration, Ts1/ Rb M/( Rb .2
M) 2M/(2M+1) Rb
Training sequence 2000 bits 300 symbols 600 symbols
RMS delay spread, Drms(ns)
10 5 2 10 5 2 10 5 2
Normalized time delay (Drms/Ts)
1.5 0.75 0.3 4 2 0.75 2.3 1.13 0.45
Delayed samples 8 4 2 22 11 6 13 7 3
Results and Discussion
0 2 4 6 8 10 12 1410
-6
10-5
10-4
10-3
10-2
10-1
100
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
Error performance for LOS links (150 Mbps)
02/2)( NbRRPSNR
Results and Discussion
Unequalized (Rb = 150Mbps, Drms = 5ns)
0 5 10 15 20 25 30 35 4010-6
10-5
10-4
10-3
10-2
10-1
100
SNR( dB)
SER
LOS
PPMD
PIMO
OK
Unequalized PPM
Unequalized DPIM
Unequalized OOK
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
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
0 5 10 15 20 25 30 35 4010-6
10-5
10-4
10-3
10-2
10-1
100
SNR( dB)
SE
R
LO
S
Unequalized
AN
N equalizer
Linear equalizer
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 .
0 5 10 15 2010
-6
10-5
10-4
10-3
10-2
10-1
100
SNR( dB)
SE
R
DPIM
DPIMPPM
PPM
OO
K
OOK
ANN Equalized
LOS
Results and Discussion
ANN Equalizer (Rb = 150Mbps, Drms = 1, 2, &10 ns)
0 5 10 15 20 2510
-6
10-5
10-4
10-3
10-2
10-1
100
SNR(dB)
SE
R DPIM
PPM
PP
M
DPIM
DPIM
PPM
10ns2ns1ns
OO
K
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)
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.
Transmitterfilter g(t)
Diffuse IR channel h(t)
ADCSlicer
Inputbits
Outputbits
XPavg
X
R
X
Artificial Intelligence
Anti-alias
(LPF)
noisen(t)
+
Wavelet Analysis
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).
3 bit sliding window
1 2 83
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
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.
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.
Issues and Future Works
Thank you!Thank you!