1 simulation of communication systems professor z. ghassemlooy optical communications research group...

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Simulation of Communication Systems

Professor Z. GhassemlooyProfessor Z. Ghassemlooy

Optical Communications Research Grouphttp://soe.unn.ac.uk/ocr/

School of Computing, Engineering and Information Sciences

University of Northumbria at Newcastle, UK

Eng. of S/W Pro., India 2009

http://soe.unn.ac.uk/ocr/


Outline of Presentation

• Communications Systems• Simulation software types• Case Studies based on Matlab• Concluding Remarks

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Northumbria University at Newcastle, UK

4

Telecommunications Research Areas


5

Photonics - Applications

Long-Haul Metropolitan Home access

Board -> Inter-Chip -> Intra-Chip

• Photonics in communications: expanding and scaling

Health(“bio-photonics”)

Environmentsensing

Securityimaging

• Photonics: diffusing into other application sectors

Optical Communications

Optical FibreCommunications

Photonic Switching

Indoor

WiredWireless

Free-Space Optics(FSO)

School of Computing, Engineering and Information Sciences – Research

• Chromatic dispersion compensation using optical signal processing• Pulse Modulations• Optical buffers• Optical CDMA

• Pulse Modulations• Equalisation• Error control coding• Artificial neural network & Wavelet based receivers

• Fast switches• All optical routers

Subcarrier modulation Spatial diversity Artificial neural network/Wavelet based receivers

6Eng. of S/W Pro., India 2009

Staff• Prof. Z Ghassemlooy• J Allen• Dr R Binns• Dr K Busawon• Dr W. P. Ng

Visiting Academics• Prof. V Ahmadi, Univ. Of Tarbiate Modaress , Tehran, Iran• Dr M. H. Aly, 2Arab Academy for Scie. and Tech. and Maritime Transport, Egypt• Prof. J.P. Barbot, France • Prof. I. Darwazeh, Univ. College London• Prof. H. Döring, Hochschule Mittweida Univ. of Applied Scie. (Germany) • Prof. E. Leitgeb, Graz Univ. of Techn. (Austria)

PhD Students•M. Amiri, A. Chaman-Motlagh, M. F. Chiang, M. A. Jarajreh, R. Kharel, S. Y Lebbe, W.

Loedhammacakra, Q. Lu, V. Nwanafio, E. K. Ogah, W. O. Popoola, S. Rajbhandari, A.

Shalaby, X. Tang

MSc and Beng: A Burton, D Bell, G Aggarwal, M Ljaz, O Anozie, W Leong , S Satkunam

OCRG – People



Simulation – Introduction

• In recent years there has been a rapid growth in application of computer simulation in communication engineering.

• Hardware becoming more complex and costly• A way forward to many researcher and teachers is to

implements ideas in the software environment. • This allows testing of the system using idealised

processing elements, which may take a significant time to design and realise in hardware.

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Simulation – Introduction

• Can support the hardware design by giving optimised component values, for the critical parts, and an early indication of the performance of the system

• Allowing users to study or try things that would be difficult or impossible in real life

• Simulations are particularly useful when a real-life process:

is too dangerous, takes too long, is too quick to study, is too expensive to create.

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Simulation Tools - Some Features

• Reliability - Depend on the validity of the simulation model, therefore verification and validation are very important

• Reproducibility of results

• User friendly, simple and flexible (allowing user defined functions)

• Extensive details of theory adopted

• High speed, precession and accuracy

• Hidden source code + Up to date library

• Debugging capabilities and Scalability

• Can readily be upgraded and updated

• Cost effective and time saving

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Simulation Tools - Disadvantages

• Poor modelling or poor data collection can lead to: • inaccuracy or • completely misleading results

• Obsession - can lead to superficial understanding and no experimental verification

• However, simulation tools have become integral part of today’s research and teaching activities• Mainly for cost reasons

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Simulation Software – Application in Engineering



Simulation Software – Key Features

• Numerical Integration procedures– E.g. Matlab has a number of procedures

• Rung-Kutta 45 – Most advanced and ideal for analogue systems• Rung-Kutta 45• Stiff Adam with a fixed step integration – Used for discrete systems• Euler – The most basic and used for slow varying discrete systems

• Ability to plot and display graphs• 2D, 3D visualisation• Simplicity for programming• Compatibility with other software

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Simulation Tools – Types

• Matlab/Simulink• Orcad/Pspice• VPI• Mathcad• OptSim ™ 4.0: simulation and design of

advanced fiber optic communication systems• OptiSystem: large scale system software• OptiFDTD

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Matlab/Simulink

• A high-performance language for technical computing• Integrates computation, visualization, and programming in

an easy-to-use environment• Typical uses include:

– Math and computation– Algorithm development– Data acquisition– Modelling, simulation, and prototyping– Data analysis, exploration, and visualization– Scientific and engineering graphics– Application development, including graphical user interface

building– Compatible with excel, uses Maple and is compatible with other

software packages such as C, C++, VPI, etc. 15


Orcad/Pspice

• To model circuits with mixed analogue and digital devices

• Software-based circuit breadboard for test and refinement

• Can perform:– AC, DC, and transient analyses– Parametric, Monte Carlo, and sensitivity/worst-case

analyses – i.e. circuit behaviour in a changing environment– Digital worst-case timing analysis : to resolve timing

problems occurring with only certain combinations of slow and fast signal transmissions, etc.

• Not compatible with excel16


Mathcad

• A desktop software for performing and documenting engineering and scientific calculations

• Equations and expressions are displayed graphically (WYSIWYG)• Capabilities :

– Solving differential equations - several possible numerical methods– Graphing functions in two or three dimensions– Symbolic calculations including solving systems of equations– Vector and matrix operations including eigenvalues and eigenvectors– Curve fitting– Finding roots of polynomials and functions– Statistical functions and probability distributions– Calculations in which units are bound to quantities

• One can’t use symbolic parameters only numerical parameters

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OptiSystem

• Is used for – designing, testing and optimization of virtually any type

of optical links in the physical layers– based on a large collection of realistic models for

components and sub-systems

• OptiFDTD (finite-difference time-domain) – propagation of optical fields through nano- to micro-

scaled devices by directly solving Maxwell’s equations numerically

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OptiSystem – contd.

• OptiBPM– Based on the beam propagation method (BPM)

• a semi-analytical technique that solves an approximation of the wave equation

– Waveguide other similar optical devices– Light propagation predominantly in one direction

over large distances

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Virtual Photonics Inc.

• Used in optical networks and optical devices modelling

• Support C and Matlab

• Will talk about this in my second lecturer!

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Case Studies - MATLAB

User SourceDecoder

ChannelDecoder

Demod-ulator

Estimate ofmessage signal

Estimate of channel code

word

Receivedsignal

Channel code word

Source SourceEncoder

ChannelEncoder

Mod-ulator

Message signalModulated

Transmitted signal

ChannelA typical communication system block diagram

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• Aim: To simulate a communication system link

Tasks: • Channel modeling• Comparing received and transmitted signals• System performance evaluation• System optimization • Final system design

Case Study 1 - AM/FM communication system s

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AM/FM Simulation - System Parameters

Know parameters• Carrier frequency, and power• Signal bandwidth• Modulation index• Channel bandwidth and loss• Link length• Transmitter/receiver antenna type and gain

Performance parameters• Output signal-to-noise vs carrier to noise ratio• System linearity• Harmonic distortions

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FM – Simulation Block Diagram

FM modulator AmplifierAmplifier TransmitterTransmitter

ChannelChannel

ReceiverReceiverAmplifierAmplifierFM demodulator

FM demodulator

Low pass filter

Low pass filter

Message

Recovered

Message

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FM Simulation - Matlab-Simulink• Provided that the mathematics underlying each block is fully

appreciated, one could use any programming languages including high level computer languages C, C++, Java or scientific programming languages Matlab, MathCAD , Mathematica, Octave to name a few

• Matlab/Simulink – One of the most popular simulation tool available– Simulink is more user friendly for beginners as there are many drag and

drop block functions.– However Simulink also sometimes limits flexibility to users.

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FM Simulation - Results

0 1 2 3 4 5-1

-0.5

0

0.5

1

Time

Am

plitu

de

message

0 1 2 3 4 5

-1

-0.5

0

0.5

1

Transmitted signa (Tx)l

Time

Am

plitu

de

0 1 2 3 4 5-1.5

-1

-0.5

0

0.5

1

1.5

Time

Am

plit

ude

Received signal (Rx)

0 1 2 3 4 5-30

-20

-10

0

10

20

30Demodulated Signal (Rm)

TimeA

mplitu

de

0 1 2 3 4 5

-1

-0.5

0

0.5

1

Time

Am

plit

ude

Recovered message (mr)

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FM Simulation - Performance Evaluation• The easiest way to evaluate the performance is by visual inspections• For example, one can hardly differentiate between the transited

message and recover message in the previous example• Message signal at different SNRs is shown below- observe the

improvement in the performance with increasing SNRs

0 1 2 3 4 5

-1

-0.5

0

0.5

1

Time

10 dB

15 dB20 dB

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FM Simulation - Performance Evaluation• Visual inspection is the simplest and in many cases gives an insight to

the system, BUT it is very error prone• Alternative method of analysis should be used• Considered error signal defined as: error = (m - mr)2

• The error signal at SNRs of 15, 20 and 40 is shown below• The performance difference between the SNRs of 15 and 20 is apparent

1 2 3 4 5 6 7 80

0.2

0.4

0.6

0.8

1x 10

-3

Time

erro

r

15 dB

20 dB40dB

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FM Simulation - Performance Evaluation• Simulation software may provide many interesting results, but the expertise

and experience of the user play's a major role• In previous plot - very little difference between 20 dB and 40 dB• An experienced user may choose the log-scale to plot error to gain more

information, shown below• Compared to the pervious plot, difference in performance for 20 db and

40 dB is clear from this plot

1 2 3 4 5 6 7 8-100

-90

-80

-70

-60

-50

-40

-30

-20

Time

Err

or (

dB)

15 dB

20 dB40 dB

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Case study 2- Digital Communications

Transmitter filterp(t)

Transmitter

n(t)

OutputBits,

Inputbits, ai

sample

Unit energy filter u(t)

(matched to p(t))

Transmitter ReceiverChannel

X(t) S(t) r(t) riiaReceiver

• Depending upon the channel, receiver may incorporated other signal processing tools like equalizing filter, low pass filter and so on

• The output bits are compared to the transmitted to bit to calculated the error

• The bit error rate (BER) is the metric used in all digital communication system to compare and evaluate the system performance

• BER depends on the SNR (valid only for particular signalling format):

SNRerfcBER2

1

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Modelling Approach

• A discrete model based on mathematical analysis is generated and model using the simulation software

• Discrete-time equivalent system of digital communication system is defined as:

ri = Eb+ni if bi=1

ri = ni if bi=0

ri is the sampled output

Eb is the energy per bit and ni is the additive white Gaussian noise

• Performance evaluation:– bit error rate– eye-diagram

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Digital Systems – Matlab Simulink

0 1 20

1

Time

Am

plitu

de

0 1 2 3 4 5-0.2

-0.1

0

0.1

0.2

0 1 2 3 4 5 6-0.2

0

0.2

0.4

0.6

0.8

1

1.2Transmitted signal

MF Output

Sampling points

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-120

-100

-80

-60

-40

-20

0

20

40

Normalised frequency

Pow

er S

pect

rum

Den

sity

(dB

)

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Digital Simulation - Performance Evaluation

• BER of different modulation techniques for indoor optical wireless system

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Digital Simulation - Notes

• To properly model the system, it is necessary to understand mathematics involved in each and every module

• Code are written to approximate the mathematical equations. The code are grouped together and put as a block for simple user interface– Example: Matlab codes for noise signal:

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Digital Simulation – Matlab CodesFixed and variable parametersclearclcclose allfs = 6.0e+6; %sampling frequency 6 MHzts = 1/fs; %Sampling timefc = ; %clock signal frequencyac:; %clock signal peak amplituden = 2*(6*fs/fc); %Maximum number of points w.r.t the 6 cycles of clock signal fcnc = 6; %Number cycls of clock signal to be showntmax= nc*tc; %Maximum number of point in 6 cycles of fcfmax = (2*n*fc/fs); %Maximum frequency rangefinal = ts*(n-1); % maximum time t = 0:ts:tmax; %time vector for sketching waveform in time domain

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Digital Simulation – Matlab Codes

Data signal generated from the Clock Signal L length (sq); %All the values of clock signal is assigned to a new variable l da = sq;%Set initial valuesout=1;temp=1;for i=1:L-1 if sq(i)== -2.5 & sq(i+1)== 2.5 %Reverse output voltage polarity temp= out * -1; out=temp; end %Change value of out to +/-1 if out>0 out=1; else out= -1; end da(i)=out; %data signal at half the clock frequencyend%Set value of final element of dada(L)=out;%Plot data signal

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Optical Wireless CommunicationAbundance of unregulated bandwidth - 200 THz in the 700-1500 nm rangeAbundance of unregulated bandwidth - 200 THz in the 700-1500 nm range

What does

It Offer

?

No multipath fading - Intensity modulation and direct

detection

No multipath fading - Intensity modulation and direct

detection

Secure transmissionSecure transmission

High data rate – In particular line of sight (in and out doors)High data rate – In particular line of sight (in and out doors)

Improved wavelength reuse capabilityImproved wavelength reuse capability

Flexibility in installationFlexibility in installation

Flexibility - Deployment in a wide variety of network architectures. Installation on roof to roof, window to window, window to roof or

wall to wall.

Flexibility - Deployment in a wide variety of network architectures. Installation on roof to roof, window to window, window to roof or

wall to wall.

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(Source: NTT)

Access Network Bottleneck


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DR

IVE

R

CIR

CU

IT

POINT APOINT APOINT BPOINT B

SIG

NA

LP

RO

CE

SS

ING

PH

OT

OD

ET

EC

TO

R

Link Range L

Free Space Optics

Cloud Rain Smoke Gases Temperature variations Fog and aerosol

The transmission of optical radiation through the atmosphere obeys the Beer-Lamberts’s law:

Preceive = Ptransmit * exp(-αL)

α : Attenuation coefficient

This equation fundamentally ties FSO to the atmospheric weather conditions


Photo-detector

array

Atmosphericchannel

Serial/parallelconverter

Subcarrier modulator

.

.Data in

d(t)

Summing circuit

.

.

DC bias

m(t) m(t)+bo

Optical transmitter

Spatial diversity combiner

Subcarrierdemodulator

Parallel/serialconverter .

.

Data out

d’(t) ir

Case Study 3: Optical Wireless Systems


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M

jjcjj twtgAtm

1

)cos()()(

Serial to Parallel

Converter

.

.

.

.

.

.

PSK modulator

at coswc1t

PSK modulator

at coswcMt

PSK modulator

at coswc2t

Σ Σ Laserdriver

)(tdInput data

g(t)

g(t)

g(t)

A1

AM

A2

m(t)

DC bias

b0

Atmopsheric channel

Subcarrier Modulation - Transmitter


Photodetector

ir

x g(-t) Sampler

PSK Demodulator

at coswc2t

PSK Demodulator

at coswcMt

Parallel to Serial

Converter

PSK Demodulator

coswc1t

)(ˆ td Output data

.

.

.

Subcarrier Modulation - Receiver

)())(1()( tntmIRtir

Photo-current

R = Responsivity, I = Average power, = Modulation index, m(t) = Subcarrier signal

2

2

2

)(

IRASNRele

42 42Eng. of S/W Pro., India 2009

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20 25 30 35 4010

-10

10-8

10-6

10-4

10-2

SNR (dB)

BE

R

DPSK

BPSK

16-PSK

8-PSK

Log intensity

variance = 0.52

0

22

)()/sin(loglog

2dIIpMMSNRQ

MBER e

BPSK based subcarrier modulation is the most power efficient

BPSK BER against SNR for M-ary-PSK for log intensity variance = 0.52

Error Performance – Bit Error Performance


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Receiver Models

TX Channel

Noise

…

+

Slicer

MF Equaliser Slicer Data out

CWT NN Slicer Data out

Data in

MMSE

Wavelet - NN

Data out



Wavelet-AI Receiver - Advantages and Disadvantages

• Complexity - many parameters & computation power

• High sampling rates- technology limited

• Speed- long simulation times on average machines

• Similar performance to other techniques• Data rate independent

- data rate changes do not affect structure (just re-train)• Relatively easy to implement with other pulse modulation

techniques


Wavelet-AI Receiver

SNR Vs. the RMS delay spread/bit duration

Wavelet


Final Remarks• Simulation software provide scientist and

engineers with additional tools to implement, assess and modify ideas with a press of a button

• Detailed mathematical understanding is essential• High speed and parallel processing is the way

forward• Should never be a substitute to real practical

systems

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Thank you for your attention !

Any questions?

Eng. of S/W Pro., India 2009Z Ghassemlooy

Acknowledgements

• To R Kharel, S Rajbhandari, W Popoola, and other PhD students,

• Northumbria University and CEIS School for Research Grants

WBU- India 09

1 simulation of communication systems professor z. ghassemlooy optical communications research group...

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