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!