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

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s, Sharif, EE, Iman G

holampour, im

angh@sharif.edu , 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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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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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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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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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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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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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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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 allMeans more abstraction

Intermediate

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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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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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Communications Systems DesignCommunications Systems Design

Simulation appears in many phases!From Design to Deployment

Design Trade-Off Studies

Parameter Optimization

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Performance Evaluation

Establishing Test Procedures

Benchmarks

End of Life Prediction

Anomaly Investigation

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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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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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Different Aspects, Knowledge

DSP Communications System Theory Numerical Analysis/ Number Theory Probability Theory, Stochastic Processes

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Either used in the system or concepts help in simulation

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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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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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- 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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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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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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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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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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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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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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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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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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Modeling of Individual BlocksModeling 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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Modeling of Individual BlocksModeling 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 invocationN>1: Efficient when invocation overhead is large

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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 optimizationexternal knobs, visible from outside

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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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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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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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Performance EstimationPerformance Estimation

Performance MeasuresAnalog: SNR, Digital BER

Monte Carlo Techniques: Pe Ne/N, N>>1Trade 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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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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