1 Simulation of Communication Systems Professor Z. Ghassemlooy Optical Communications Research Group School of Computing, Engineering and Information Sciences University of Northumbria at Newcastle, UK Eng. of S/W Pro., India 2009

Outline of Presentation Communications Systems Simulation software types Case Studies based on Matlab Concluding Remarks 2

Eng. of S/W Pro., India Northumbria University at Newcastle, UK

4 Telecommunications Research Areas Eng. of S/W Pro., India 2009

5 Photonics - Applications Long-Haul MetropolitanHome access Board -> Inter-Chip -> Intra-Chip Photonics in communications: expanding and scaling Health (“bio-photonics”) Environment sensing Security imaging Photonics: diffusing into other application sectors

Optical Communications Optical Fibre Communications Photonic Switching Indoor Wired Wireless Free-Space Optics (FSO) 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 6 Eng. 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, 2 Arab 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 7 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. 8

Eng. of S/W Pro., India 2009 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. 9

Eng. of S/W Pro., India 2009 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 10

Eng. of S/W Pro., India 2009 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 11

Component: modelling and characterisation System or subsystem: simulation and behaviour analysis, and automation Data logging and acquisition Real time applications Simulation Software – Application in Engineering 12 Eng. of S/W Pro., India 2009

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 13

Eng. of S/W Pro., India 2009 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 14

Eng. of S/W Pro., India 2009 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

Eng. of S/W Pro., India 2009 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 16

Eng. of S/W Pro., India 2009 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 17

Eng. of S/W Pro., India 2009 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 18

Eng. of S/W Pro., India 2009 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 19

Eng. of S/W Pro., India 2009 Virtual Photonics Inc. Used in optical networks and optical devices modelling Support C and Matlab Will talk about this in my second lecturer! 20

Eng. of S/W Pro., India 2009 Case Studies - MATLAB User Source Decoder Channel Decoder Demod- ulator Estimate of message signal Estimate of channel code word Received signal Channel code word Source Encoder Channel Encoder Mod- ulator Message signal Modulated Transmitted signal Channel A typical communication system block diagram 21

Eng. of S/W Pro., India 2009 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 22

Eng. of S/W Pro., India 2009 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 23

Eng. of S/W Pro., India 2009 FM – Simulation Block Diagram FM modulator Amplifier Transmitter Channel Receiver Amplifier FM demodulator Low pass filter Message Recovered Message 24

Eng. of S/W Pro., India 2009 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. 25

Eng. of S/W Pro., India 2009 FM Simulation - Results 26

Eng. of S/W Pro., India 2009 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 27

Eng. of S/W Pro., India 2009 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 - m r ) 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 28

Eng. of S/W Pro., India 2009 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 29

Eng. of S/W Pro., India 2009 Case study 2- Digital Communications 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): 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: r i = E b +n i if bi=1 r i = n i if bi=0 r i is the sampled output E b is the energy per bit and n i is the additive white Gaussian noise Performance evaluation: – bit error rate – eye-diagram 31

Eng. of S/W Pro., India 2009 Digital Systems – Matlab Simulink 32

Eng. of S/W Pro., India 2009 Digital Simulation - Performance Evaluation BER of different modulation techniques for indoor optical wireless system 33

Eng. of S/W Pro., India 2009 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: 34

Eng. of S/W Pro., India 2009 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 35

Eng. of S/W Pro., India 2009 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 36

Eng. of S/W Pro., India Optical Wireless Communication Abundance of unregulated bandwidth THz in the nm range What does It Offer ? No multipath fading - Intensity modulation and direct detection Secure transmission High data rate – In particular line of sight (in and out doors) Improved wavelength reuse capability Flexibility 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.

38 (Source: NTT) Access Network Bottleneck Eng. of S/W Pro., India 2009

39 DRIVER CIRCUIT POINT A POINT B SIGNAL PROCESSING PHOTO DETECTOR 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: P receive = P transmit * exp(-αL) α : Attenuation coefficient This equation fundamentally ties FSO to the atmospheric weather conditions Eng. of S/W Pro., India 2009

Photo- detector array Atmospheric channel Serial/parallel converter Subcarrier modulator.... Data in d(t)d(t) Summing circuit.... DC bias m(t)m(t) m(t)+b o Optical transmitter Spatial diversity combiner Subcarrier demodulator Parallel/serial converter.... Data out d’(t) irir Case Study 3: Optical Wireless Systems 40 Eng. of S/W Pro., India 2009

41 Subcarrier Modulation - Transmitter Eng. of S/W Pro., India 2009

Subcarrier Modulation - Receiver Photo-current R = Responsivity, I = Average power,  = Modulation index, m(t) = Subcarrier signal 42 Eng. of S/W Pro., India 2009

43 BPSK based subcarrier modulation is the most power efficient BPSK BER against SNR for M-ary-PSK for log intensity variance = Error Performance – Bit Error Performance Eng. of S/W Pro., India 2009

44 Receiver Models TX Channel Noise … + Slicer MF Equaliser Slicer Data out CWT NN Slicer Data out Data in MMSE Wavelet - NN Data out Eng. of S/W Pro., India 2009

45 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 Wavelet-AI Receiver SNR Vs. the RMS delay spread/bit duration Wavelet

Eng. of S/W Pro., India 2009 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 47

Eng. of S/W Pro., India Thank you for your attention ! Any questions?

Eng. of S/W Pro., India 2009 Z Ghassemlooy Acknowledgements To R Kharel, S Rajbhandari, W Popoola, and other PhD students, Northumbria University and CEIS School for Research Grants WBU- India 09