simulation of communication systems
DESCRIPTION
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. Outline of Presentation. - PowerPoint PPT PresentationTRANSCRIPT
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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
Eng. of S/W Pro., India 2009
Outline of Presentation
• Communications Systems• Simulation software types• Case Studies based on Matlab• Concluding Remarks
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Eng. of S/W Pro., India 20093
Northumbria University at Newcastle, UK
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Telecommunications Research Areas
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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
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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
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Eng. of S/W Pro., India 2009
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
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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
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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
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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
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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
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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
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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
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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
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Wavelet-AI Receiver
SNR Vs. the RMS delay spread/bit duration
Wavelet
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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