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Simulation of Simulation of Communications Systems, 25178Communications Systems, 25178
Syllabus
1) Introduction to Simulation and Modeling
2) Role of Simulation in Communications Systems Life Cycle
3) Simulation Methodology
4) Practical Issues in Simulation of Communication Systems
5) Representation of Signals and Systems in Simulation Environment
6) Modeling and Simulation of Communications Systems Elements
7) Generation of Data Signals, Random Numbers and Processes
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7) Generation of Data Signals, Random Numbers and Processes
8) Modeling and Simulation of Non-linearities
9) Modeling and Simulation of Time Varying Systems
10) Modeling & Simulation of Communication Channels (Waveform/Discrete Channels)
11) Monte Carlo Methods
12) Rare Events Simulation and Importance Sampling Acceleration in MC Methods
13) Semi-Analytic Methods in Simulation of Communication Systems
14) Advanced Simulation Techniques: Tail Extrapolation, pdf Estimators, Splitting …
15) Case Studies
Course N
otes, Simulation of C
ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Simulation of Simulation of Communications Systems, 25178Communications Systems, 25178
Text Book and References
1) “Principles of Communication Systems Simulation with Wireless Applications”, W. H. Tranter, K. S. Shanmugan, T. S. Rappaport, K. L. Kosbar, Prentice Hall, 2004, ISBN 0-13-494790-8.
2) “Simulating Wireless Communication Systems: Practical Models in C++”, C. B. Rorabaugh, Prentice Hall, 2004, ISBN: 0-13-022268-2.
3) “Rare Event Simulation using Monte Carlo Methods”, G. Rubino, B. Tuffin, John Wiley and Sons, 2009, ISBN: 978-0-470-77269-0.
4) “Modelling the Wireless Propagation Channel: A simulation approach with Matlab”, F. P Fontan, P. M. Espineira, John Wiley and Sons, 2008, ISBN: 978-0-470-72785-0.
5) “Introduction to communication systems simulation”, M. Schiff, Artech House, 2006, ISBN-101-59693-002-0.
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5) “Introduction to communication systems simulation”, M. Schiff, Artech House, 2006, ISBN-101-59693-002-0.
6) “Simulation of Communication Systems, Modeling, Methodology, and Techniques”, M. C. Jeruchim, P. Balaban, K. S. Shanmugan, Cluwer Academic Publishers, 2nd Edition 2002, ISBN 0-306-46267-2.
7) “Simulation Techniques, Models of Communications, Signals and Process”, F.M. Gardner, J. D. Baker, John Wiley & Sons Inc. 1997, ISBN 0-471-51764-9.
8) “Contemporary Communication Systems Using Matlab and Simulink”, J.G. Proakis, M Salehi, G. Bauch, CL-Engineering 2003, ISBN 0-534-40617-3.
9) “Telecommunications Breakdown”, C. R. Johnson, Jr., W.A. Sethares, Prentice Hall, 2004, ISBN: 0-131-43047-5.
10) “Algorithms for Communications Systems and their Applications”, N. Benvenuto, John Wiley & Sons Inc. 2003, ISBN 0-470-84389-6.
11) Selected papers and book chapters
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s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
SimulationSimulation of of Communications Communications SystemSystemss
�� Simulation Simulation is the act of imitating the behavior of some situation
or some process by means of something suitably analogous
In computer science, the technique of representing the real world by a computer program
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�� ((Telecommunications) Communications Telecommunications) Communications is a process of is a process of transferring transferring informationinformation from one entity to anotherfrom one entity to another
�� System: System: a group of independent but inter-related elements
comprising a unified whole
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Etymology: 14Etymology: 14thth --1717thth CenturyCentury
Middle English � Old French� Latin
Middle English Middle English �� GreekGreek
�� Simulation: Similar, likeSimulation: Similar, like
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�� Communication: make it common, share locallyCommunication: make it common, share locally
�� Information: from Informing, Giving Shape to MindInformation: from Informing, Giving Shape to Mind
�� System: System: animal body as an organized whole, sum of the
vital processes in an organism
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Communication SystemsCommunication SystemsFidelity ‹–› Complexity ‹–› Spectral Efficiency
Complexity Aspects
1) Architecture
2) Hostile Deployment Environment
3) High Data Rates, High Quality
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3) High Data Rates, High Quality
4) Limited Bandwidth, Power, Size, …
Complex Techniques for Modulation, Pulse Shaping, Source
and Channel Coding, Interleaving, Equalization,
Synchronization, Carrier Recovery, …
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s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
ModelsModels
First Step to Study a System … Art of ModelingTo Develop a Behavioral Model
Model: An Abstraction of a Real System to Predict andFormulate the System BehaviorCaptures the in/out behavior of the system under specific conditions
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Captures the in/out behavior of the system under specific conditions
Often Mathematical (Formulas, Relations, Logic)
Physical Systems Translate to Mathematical Systems thru Models
Accuracy versus Simplicity (Modeling Trade-off)
1) Analytical Models � Usually Continuous
2) (Measurement Models) sampling/quantization
3) Simulation Models � Mostly Discrete
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Modeling Validation & SolutionModeling Validation & Solution
� Modeling Validation
1. Reexamining the Formulation of the problem
2. Consistent Dimensionality of Math Expressions
3. Varying the Input Checking the Output
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4. Retrospective Test
5. Prospective Test
� Modeling Solution
1. Analytical
2. Numerical 7
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Simulation ModelsSimulation Models
Physical Entity
Abstraction Accuracy
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Many Assumptions
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Analytical Model
Simulation Model
Complexity
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Continuous
Discrete
More!
Divide and ConquerDivide and Conquer
System
Block1
Block2
Input
Output
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2
Block3
We might be interested in some intermediate parameters
(signals/states), not all
Means more abstraction
Intermediate
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Simulation ModelsSimulation Models
1. Static versus Dynamic Models
State variables do not depend on time
2. Deterministic versus Stochastic Models
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State variables are fixed or non-random
3. Continuous versus Discrete Models
State variables are defined in all times
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Simulation RolesSimulation Roles
� System behavior and life cycle predictions� Parametric studies� What-if questions� Design: trade-off studies � bit-true validation� Performance evaluation
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� Performance evaluation� Measurements, test procedures� Rare conditions/cases� Graphical view of signals� Comparisons� Deployment anomaly investigation
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Communications Systems DesignCommunications Systems Design
Simulation appears in many phases!From Design to Deployment
� Design Trade-Off Studies
� Parameter Optimization
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ommunication System
s, Sharif, � Parameter Optimization
� Performance Evaluation
� Establishing Test Procedures
� Benchmarks
� End of Life Prediction
� Anomaly Investigation
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
D&D Process D&D Process Communications SystemsCommunications Systems
� Statement/Analysis of user requirements and performance expectations (MRD)
� System engineering� System design (blueprint)
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ommunication System
s, Sharif, � System design (blueprint)� Implementation and testing of key components
� Completion of HW prototype� Validation of simulation model� End of life prediction
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Different Aspects, Knowledge
� DSP� Communications System Theory� Numerical Analysis/ Number Theory� Probability Theory, Stochastic Processes
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ommunication System
s, Sharif, � Probability Theory, Stochastic Processes� Estimation� Computer Science…
Either used in the system or concepts help in simulation
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Simulation SW Packages1. Model Builder2. Model Library3. Model User Interface (may be a GUI)4. Simulation Kernel: data driven, time driven, event driven5. Postprocessors
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Choose/Build the SIM models (1,2)Add the SIM Parameters (3)Choose Design Parameters (3)Simulation Stop/End/Completion (4)Post-Processing (5) : Display (Waveform plot, Spectral Plot, Scatter Plot,
Eye Diagram, …), Analysis, …
Simulation � Low level C/C++ � Bit-True C � HDL or ASM
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Simulation MethodologySimulation Steps:
1) Quantitative
Science of simulation
2) Qualitative
Methodology or the art of simulation
- Basic Purpose of Com Systems: Process Waveforms and Symbols
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s, Sharif, - Basic Purpose of Com Systems: Process Waveforms and Symbols
- Simulation of Com Systems: Generating and processing of the sampled values
Fundamental Simulation Steps:
1) Mapping the problem into a simulation model
2) Decomposing the problem into a set of smaller ones
3) Selecting appropriate set of techniques to solve sub-problems
4) Combining the sub-problems solutions to solve the main one 16
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Problem Mapping Techniques Problem Mapping Techniques (comments on 1)(comments on 1)
Generic ThemeStart with “clear statement of the problem”
Include everything that you can think of in the initial block diagram
A) Hierarchical Representation
B) Partitioning and Conditioning
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s, Sharif, B) Partitioning and Conditioning
C) Simplifications (approximations/assumptions)
Managing the complexity
in two directions
Vertical: Layers
Horizontal: Partitions
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L1 System
L2 Sub-systems
L3 Components
L4 Physical
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Hierarchical RepresentationVertical, Layers
Back annotation: create higher level models from details of lower layer model and replace…
Co-simulation: using a separate simulation to prepare higher layer model
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Envelope Detector
AM Demodulator
AmpNon
linearity LPF
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Partitioning and Conditioning
� Partitioning
Separating the task in the same layer of abstraction
Main complex problem � A set of interrelated/independent problems � Simulate separately and combine
� Conditioning
Fix the condition or state of a portion of the system
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ommunication System
s, Sharif, Fix the condition or state of a portion of the system
Simulate the rest
Repeat for different states and conditions
(parts are simulated separately)
Main results derived by averaging
f(a,b) = f(a) f(b|a) f(a)� SIM1 f(b|a)� SIM2
E[g(A,B)] = ∫∫ g(a,b) f(a,b)da db= ∫f(a){∫g(a,b)f(b|a) db} da19
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
SimplificationSimplification
Too much details in the block diagram� Omission of blocks with no significant impacts� Approximations (linearity, time-invariance, …)
Example: Quasi static cases
� Combining Blocks
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� Combining BlocksWhen intermediate details are not importantExample: Performance estimation
In AM detector: For SNR calculation no need to consider the filter circuit.
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Modeling of Individual Blocks Modeling of Individual Blocks (comments on 2)(comments on 2)
Generic Block:{ y[k], y[k-1], …, y[k-m] } =
F{x[k-j], x[k-j-1], …, x[k-j-n]; k; p1, …pq}
Producing m samples per invocation
‘F’ independent of k ~ time invariant
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s, Sharif, ‘F’ independent of k ~ time invariant
m>0 ~ block input/output
m=0 ~ sample by sample model
n=0 ~ memory-less
‘F’ linear or nonlinear
Band limited or unlimited
Time Domain or Frequency Domain or Transform Domain
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Modeling of Individual Blocks…Modeling of Individual Blocks…
Generic Methods…� Interface to Other Blocks
Consistency, Compatibility between blocks
Well-defined and Well documented interfaces
Probable Problems:
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Inconsistency in different domains of processing
� Signal types
� Block size
� Step size
Inconsistency of parameters specification in different blocks
…………
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Modeling of Individual Blocks…Modeling of Individual Blocks…
Generic Methods…� Choosing the Sampling RateLP-equivalent BW x 2
Unlimited: Analog (3dbBW x 8 to16), Digital (Symbol rate x 8to16)
� Block processing, one or “N” sample per invocation
“N>1”: Efficient when invocation overhead is large
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s, Sharif, “N>1”: Efficient when invocation overhead is large
delay of NT, complicated when non-linearity and/or feedback, needs scheduling when different blocks have different “N”
� Variable Step-Size ProcessingMulti-rate Sampling, Buffering, Interpolation/Decimation
� Parameterization, for design optimization“external knobs”, visible from outside
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Selecting appropriate … Selecting appropriate … (comments on 3)(comments on 3)
Selecting an appropriate set of modeling simulation and estimation techniques to solve sub problems
Can be rigorous, algorithmic, well defined
Or “Tricks of the trade”
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s, Sharif, Input waveform Output waveform
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Block
Generation of random numbers and waveforms
Analysis (+ analysis parameters): Inline: Estimation during SimulationOffline: Estimation after Simulation
From Library + Setting Parameters
Input waveform Output waveform
Combining … Combining … (comments on 4)(comments on 4)
Supporting Interconnections requires some methods
Re-sampling, format, type, space, system theory tools
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Block3Block2Block1
We also need validation:
1) Analytically if possible (Theory)
2) Based on some measurements (Experiment)
3) Based on Intuition (Engineering vision)
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Random Process Modeling and SimulationRandom Process Modeling and Simulation
Generating input waveforms, noise, interference to drive the simulation models
All are random in Nature
Need some fidelity measures
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Methodology
� Gaussian approximation (Central limit theorem)
� Equivalent Process Representation
� Slow versus Fast Processes◦ Moderately different: Down-sampling/up-sampling
◦ Highly different: Partition and condition on the slow process
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otes, Simulation of C
ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
Performance EstimationPerformance Estimation
� Performance MeasuresAnalog: SNR, Digital BER
� Monte Carlo Techniques: Pe ≈ Ne/N, N>>1
Trade off: Accuracy and Simulation Run time
Unbiased and Consistent Estimation?
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Unbiased and Consistent Estimation?
It is unbiased, if some conditions are met
Variance reduction techniques
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System
D
ComparisonCount errors
Bit Stream
BER
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011
MATLAB / SIMULINKMATLAB / SIMULINKCourse N
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Base of our examples and exercisesWill learn how to use them efficiently …
• MEX Functions• Nested Functions• GUI• and more…
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ommunication System
s, Sharif, EE, Iman G
holampour, im
[email protected] , Fall 2011