Simulation of Communication
Systems
Professor Z. Ghassemlooy
Optical Communications Research Group
http://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
1
Outline of Presentation
•
•
•
•
Communications Systems
Simulation software types
Case Studies based on Matlab
Concluding Remarks
Eng. of S/W Pro., India 2009
2
Northumbria University at Newcastle, UK
Eng. of S/W Pro., India 2009
3
Telecommunications Research Areas
Eng. of S/W Pro., India 2009
4
Photonics - Applications
• Photonics in communications: expanding and scaling
Long-Haul
Metropolitan
Home access
Board -> Inter-Chip -> Intra-Chip
• Photonics: diffusing into other application sectors
Health
(“bio-photonics”)
Environment
sensing
Security
imaging
5
School of Computing, Engineering and Information
Sciences – Research
Optical Communications
Wireless
Wired
Optical Fibre
Communications
• Chromatic dispersion
compensation using
optical signal processing
• Pulse Modulations
• Optical buffers
• Optical CDMA
Eng. of S/W Pro., India 2009
Photonic
Switching
• Fast switches
• All optical routers
Indoor
• Pulse Modulations
• Equalisation
• Error control coding
• Artificial neural network &
Wavelet based receivers
Free-Space
Optics
(FSO)
 Subcarrier modulation
 Spatial diversity
 Artificial neural
network/Wavelet
based receivers
6
OCRG – People
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:
Eng. of S/W Pro., India 2009
A Burton, D Bell, G Aggarwal, M Ljaz, O Anozie, W Leong , S Satkunam
7
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.
Eng. of S/W Pro., India 2009
8
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 reallife process:
is too dangerous,
takes too long,
is too quick to study,
is too expensive to create.
Eng. of S/W Pro., India 2009
9
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
Eng. of S/W Pro., India 2009
10
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
Eng. of S/W Pro., India 2009
11
Simulation Software – Application in
Engineering
Eng. of S/W Pro., India 2009
Component:
modelling and
characterisation
System or
subsystem:
simulation and
behaviour
analysis, and
automation
Data logging and
acquisition
Real time
applications
12
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
Eng. of S/W Pro., India 2009
13
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
Eng. of S/W Pro., India 2009
14
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.
Eng. of S/W Pro., India 2009
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 excel
Eng. of S/W Pro., India 2009
16
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
Eng. of S/W Pro., India 2009
17
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
Eng. of S/W Pro., India 2009
18
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
Eng. of S/W Pro., India 2009
19
Virtual Photonics Inc.
• Used in optical networks and optical devices
modelling
• Support C and Matlab
• Will talk about this in my second lecturer!
Eng. of S/W Pro., India 2009
20
Case Studies - MATLAB
Channel code
word
Message signal
Source
Encoder
Source
Channel
Encoder
Modulator
A typical communication system
block diagram
Source
Decoder
User
Estimate of
message signal
Eng. of S/W Pro., India 2009
Channel
Decoder
Modulated
Transmitted
signal
Channel
Demodulator
Estimate of
channel code
word
Received
signal
21
Case Study 1 - AM/FM communication system s
• Aim: To simulate a communication system link
Tasks:
• Channel modeling
• Comparing received and transmitted signals
• System performance evaluation
• System optimization
• Final system design
Eng. of S/W Pro., India 2009
22
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
Eng. of S/W Pro., India 2009
23
FM – Simulation Block Diagram
Message
FM
modulator
Amplifier
Transmitter
Channel
Recovered Low pass
filter
Message
Eng. of S/W Pro., India 2009
FM
demodulator
Amplifier
Receiver
24
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.
Eng. of S/W Pro., India 2009
25
FM Simulation - Results
Transmitted signa (Tx)l
1
Amplitude
0.5
message
1
0
-0.5
Amplitude
0.5
-1
0
0
1
2
3
4
5
Time
Demodulated Signal (Rm)
30
-0.5
20
0
1
2
3
4
10
5
Amplitude
-1
Time
0
-10
Received signal (Rx)
1.5
-20
1
-30
0
1
2
3
4
5
Time
Amplitude
0.5
Recovered message (mr)
0
1
-0.5
Amplitude
0.5
-1
-1.5
0
1
2
3
Time
4
5
0
-0.5
-1
0
Eng. of S/W Pro., India 2009
1
2
3
Time
4
5
26
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
10 dB
15 dB
20 dB
1
0.5
0
-0.5
-1
0
Eng. of S/W Pro., India 2009
1
2
3
Time
4
5
27
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
-3
1
x 10
15 dB
20 dB
40dB
0.8
error
0.6
0.4
0.2
0
Eng. of S/W Pro., India 2009
1
2
3
4
Time
5
6
7
8
28
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
-20
15 dB
20 dB
40 dB
-30
Error (dB)
-40
-50
-60
-70
-80
-90
-100
Eng. of S/W Pro., India 2009
1
2
3
4
5
Time
6
7
8
29
Case study 2- Digital Communications
Transmitter
Input
bits, ai Transmitter X(t)
filter
Transmitter
p(t)
Channel
Receiver
r(t) Unit energy
S(t)
Receiver
filter u(t)
(matched to
p(t))
ri
Output
Bits, â
i
sample
n(t)
• 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):
1
BER 
erfc SNR
2
30

Eng. of S/W Pro., India 2009

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
r i = 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
Eng. of S/W Pro., India 2009
31
Digital Systems – Matlab Simulink
1.2
Amplitude
1
0.2
1
0.1
0.8
Transmitted signal
MF Output
Sampling points
0.6
0
0.4
-0.1
0.2
-0.2
0
0
1
0
1
2
2
3
4
5
0
-0.2
Time
0
1
2
3
4
5
6
40
Power Spectrum Density (dB)
20
0
-20
-40
-60
-80
-100
-120
0
0.1
0.2
0.3
Eng. of S/W Pro., India 2009
0.4
0.5
0.6
Normalised frequency
0.7
0.8
0.9
1
32
Digital Simulation - Performance Evaluation
• BER of different modulation techniques for indoor optical wireless system
Eng. of S/W Pro., India 2009
33
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:
Eng. of S/W Pro., India 2009
34
Digital Simulation – Matlab Codes
Fixed and variable parameters
clear
clc
close all
fs = 6.0e+6;
%sampling frequency 6 MHz
ts = 1/fs;
%Sampling time
fc = ;
%clock signal frequency
ac:;
%clock signal peak amplitude
n = 2*(6*fs/fc); %Maximum number of points w.r.t the 6 cycles
of clock signal fc
nc = 6;
%Number cycls of clock signal to be shown
tmax= nc*tc;
%Maximum number of point in 6 cycles of fc
fmax = (2*n*fc/fs);
%Maximum frequency range
final = ts*(n-1);
% maximum time
t = 0:ts:tmax; %time vector for sketching waveform in time
domain
Eng. of S/W Pro., India 2009
35
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 values
out=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 frequency
end
%Set value of final element of da
da(L)=out;
%Plot data signal
Eng. of S/W Pro., India 2009
36
Optical Wireless Communication
Abundance of unregulated bandwidth - 200 THz in the 700-1500
nm range
No multipath fading - Intensity modulation and direct
detection
What
does
It
Offer
?
High data rate – In particular line of sight (in and out
doors)
Improved wavelength reuse capability
Flexibility in installation
Secure transmission
Flexibility - Deployment in a wide variety of network architectures.
Installation on roof to roof, window to window, window to roof or
wall to wall.
37
Eng. of S/W Pro., India 2009
Access Network Bottleneck
(Source: NTT)
Eng. of S/W Pro., India 2009
38
Free Space Optics
SIGNAL
PROCESSING
Cloud
Rain
Smoke
Gases
Temperature variations
Fog and aerosol
PHOTO
DETECTOR
DRIVER
CIRCUIT






The transmission of optical radiation through the atmosphere obeys
the Beer-Lamberts’s law:
Preceive = Ptransmit * exp(-αL)
α : Attenuation coefficient
POINT A
This equation fundamentally ties FSO to the
atmospheric weather conditions
POINT B
Link Range L
Eng. of S/W Pro., India 2009
39
Case Study 3: Optical Wireless Systems
DC bias
m(t)
d(t)
Data in Serial/parallel
converter
.
.
Subcarrier
modulator
.
.
m(t)+bo
Summing
circuit
Optical
transmitter
Atmospheric
channel
ir
d’(t)
.
.
Parallel/serial
Data out
converter
Subcarrier
demodulator
Spatial
diversity
combiner
Photodetector
array
40
Eng. of S/W Pro., India 2009
Subcarrier Modulation - Transmitter
A1
A2
Input
data
d (t )
Serial to
Parallel
Converter
.
.
.
.
.
.
AM
Eng. of S/W Pro., India 2009
g(t)
PSK modulator
at coswc1t
g(t)
PSK modulator
at coswc2t
m(t ) 
M
 A j g (t ) cos(wcj t   j )
j 1
Σ m(t)
Σ
Laser
driver
Atmopsheric
channel
DC bias
b0
g(t)
PSK modulator
at coswcMt
41
Subcarrier Modulation - Receiver
PSK Demodulator
SNRele 
( IRA )
2 2
2
x
g(-t)
Sampler
coswc1t
Photodetector
ir
PSK Demodulator
at coswc2t
Parallel
to Serial
Converter
dˆ (t )
Output
data
.
.
.
Photo-current
PSK Demodulator
at coswcMt
ir (t )  R I (1  m(t ))  n(t )
R = Responsivity, I = Average power,  =
Modulation index, m(t) = Subcarrier signal
Eng. of S/W Pro., India 2009
42
42
Error Performance – Bit Error Performance
BPSK BER against SNR for M-ary-PSK for log intensity variance = 0.52
DPSK
BPSK
16-PSK
8-PSK
-2
10
10
BER
BPSK based subcarrier
modulation is the most
power efficient
Log intensity
-4
variance = 0.52
-6
10
BER 
-8
10


2 
Q SNRe log 2 M sin( / M ) p( I )dI
log 2 M 0
-10
10
20
30
25
SNR
Eng. of S/W Pro., India 2009
35
40
(dB)
43
Receiver Models
Data in
TX
Channel
+
Noise
Data out …
Slicer
MMSE
Data out
Slicer
Equaliser
MF
Data out
Slicer
NN
CWT
Wavelet - NN
Eng. of S/W Pro., India 2009
44
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
Eng. of S/W Pro., India 2009
45
Wavelet-AI Receiver
Wavelet
SNR Vs. the RMS delay spread/bit duration
Eng. of S/W Pro., India 2009
46
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
Eng. of S/W Pro., India 2009
47
Thank you for your attention !
Any questions?
Eng. of S/W Pro., India 2009
48
Acknowledgements
• To R Kharel, S Rajbhandari, W Popoola, and other
PhD students,
• Northumbria University and CEIS School for
Research Grants
WBU- India 09
Eng. of S/W Pro., India 2009
Z Ghassemlooy