Simulations and Tools for TelecommunicationTietoliikenteen simuloinnit ja työkalut 521365S

Harri SaarnisaariPhone: 5532832

Email: harri.saarnisaari@ee.oulu.fi

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Course Description

• Lectures– Wed 10:15 – 12:00 TS126 starting 22.2.2006

• Course includes– Lectures– Compulsory simulation exercise (with Simulink)– Final exam

• Lectures include – theoretical part– Simulation tools introduction part

• Details, schedule and notes at lecturer’s home page– http://www.ee.oulu.fi/~harza

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Course Description …

• Lectures answer to questions– Why we simulate?– When we simulate?– How we simulate?

• Lectures consider simulations of– Communication systems– Protocols– Algorithms– Transceiver RF/IF-parts

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Course Description …

• Theoretical part includes– Modeling of communication systems using

simulations– Simulation methods– Confidence limits of simulations– Noise and random number generation– Modeling of fading channel

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Course Description …

• Introduction to simulation tools part includes– MATLAB (Harri Saarnisaari)

• Algorithm simulation– SIMULINK (Pasi Maliniemi)

• Algorithm and system simulation (build on Matlab)– ADS (Timo Kumpuniemi)

• RF-simulation tool– OPNET (Jarmo Prokkola)

• Network simulation tool– CSS, cocentric system studio (Juha-Pekka Mäkelä)

• Link level simulation tool– HFSS/MICROWAVE (N.N)

• Microwave simulation tool– SUPERNEC (Veikko Hovinen) ??

• Antenna simulation tool

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Course Description …

• Compulsory exercise includes– Simulation exercise with Simulink– Managed by Pasi Maliniemi

http://www.ee.oulu.fi/~pamalini– Has to be passed acceptably before credit units are

earned• Final exam

– Some easy theoretical questions– 26.5

• Credit points– 3.5

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Course Description …

• Course book– Michel C. Jeruchim, Philip Balaban & K. Sam Shanmugan: “Simulation of Communication

Systems: Modeling, Methodology and Techniques”, Kluwer Academic/Plenum Publishers, 907 s., 2000.

– The course uses the following parts of the book• Chapter 1: 112 (12 s)• Chapter 2: 1354 (42 s)• Chapter 3: 5563, 7491 (27 s) • Chapter 4: 181192 , 195198 (16 s)• Chapter 6: 289294, 308316, 320, 328334, 350356 (30 s)• Chapter 7: 371379, 383389, 392393 (18 s)• Chapter 8: 407410, 534540 (11 s)• Chapter 9: 545563, 572576, 614621 (32 s)• Chapter 10: 625, 636642, 655657, 664667 (15 s)• Chapter 11: 669, 678688, 693694, 696, 710716, 737742, 757758 (30 s)• Chapter 12: 763793 (31 s)

• These are good-to-know background material, and may involve material asked in the exam

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Course Description …

• Material available– Notes at lecturer’s home page

• as soon as they are ready– Notes and mentioned pages of the book in the

Tutor-room and Telecommunications Laboratory library

• Contact– You may contact me by phone or email and ask for

meeting– Best times are Wednesdays before or just after the

lectures

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Simulation

• Definition (a possible)– The discipline whose objective is to imitate one or

more aspects of reality in a way that is as close to that reality as possible

• Synonym (sometimes used) to simulation– Artificial reality (man made reality)

• Motivation behind simulation – It is a way to “try things out” before building the

real thing• Simulations concern how the waveforms or

signals flow through the system– How subsystems (blocks) and their different

parameters affect the system’s performance?

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Simulation …

• Use and importance of simulations has been grown recently since the digital computers have developed and became more powerful– More complex and more real things can be

simulated– Difference between simulations and reality has

been decreased

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Simulation …

• The goal in any system development is to do it in a timely, cost-effective and effort-free manner

• Communication systems have become more complex– More complicated overall systems and operating

environments– More complicated signal processing– More complicated microwave & other devices

• As a consequence, computer-aided analysis and design is the only way to achieve the goal

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Simulation …

• In the system development one has to design a system to meet some requirements and often has some limitations

• The designer usually can select possible candidate solutions based on his/her prior knowledge and/or analytical results– These analytical results are usually obtained using simplified

assumptions of reality (often oversimplified)– The used formulas are often evaluated using digital computers,

although simple paper-based rules also exist in some cases– These formula-based techniques provide considerable insight

to the problem but cannot solve all questions

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Simulation …• The designer then creates a simulation model

that are closer the reality than simplified analytical results– Based on simulation results he/she chooses a

smaller set of candidate solutions• Piece of hardware (a prototype) is then (possibly)

build to verify critical parts and/or new technology– Measured demonstrator/prototype parameters may

be used in simulations to increase the simulation accuracy

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Simulation …

• Differences between simulation and measured results?– Due to errors/simplifications in the simulations?– Due to errors or incapability to produce in

prototyping?• The goal is to build the required hardware, but this

may fail – Build hardware is different than the simulated one

• One has to find out the source for the difference before continuing the process and then solve the problem or find another solution

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Simulation …

• Once all the aspects are considered and designed satisfactory (requirements and limitations are satisfied), the product may be build

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Simulation …

• In general, the designer has three tools– Formula based– Simulation– Prototyping

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Simulation …

• All can be used separately but usually a hybrid of these is used, and it is most often the most powerful way– Formula based method is not sufficient since it simplifies

real life effects– Prototyping is the most close the reality, but it is often time-

consuming, expensive and has a limited flexibility– Simulations are flexible (parameters can be changed rather

freely), they are rather close to reality (if the used model is) but a disadvantage is that more complex simulation models require more powerful computers and/or more computation time

– Increased simulation capabilities (quality in terms of reality) have caused a trend that prototyping is reduced as much as possible; some even dream to build products directly after simulation

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Simulation …

• Analysis is still a valid and powerful method– Results are usually valid for several parameter values– More complex analysis can nowadays be made since

analysis methods develop and more powerful computer tools become available

• Simulation results cannot usually be generalized to different parameters, but the simulations show what happens with those particular parameters

• Simulations are also used to verify analysis results, i.e., to check are the made simplifications too misleading or not

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Simulation …

• The simulation model usually includes changeable parameters

• The designer simulates the effects of the parameters to the performance of the systems and selects between the parameters and/or makes trade-offs since different parameters may affect differently to the different parts of the system

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Simulation …

• The system is usually build of several subsystems• These subsystems are modeled and the signal (or its

relevant features) is passed through the models• Simulations are also used to verify a subsystem (block) or a

set of subsystems, not the whole system• However, the subsystem designer should be aware of

effects of this subsystem to other subsystems/overall system to avoid “bad” parameter selections (from point of view of the other subsystems/overall system)– Good overall picture on designer’s research/work field is

important to him/her

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Simulation …

• The accuracy (how close to the reality it is) of the simulation depends on the accuracy of the models– Erroneous models yield misleading results, conclusions and

selections and may be costly– Simulation models should correspond the reality tried to

imitate!– The error should be in an acceptable level– Avoid using misleading models!

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Simulation …

• Models may be obtained from existing knowledge of the subsystems– E.g., RF-devices used to build real systems

• One may also found a model using simplified analysis of the subsystem– E.g. assume that subsystem is linear although in

reality it may be non-linear• If subsystem model is totally unknown, it may be

measured– E.g., measurement of a radio channel

characteristics for the current problem

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Simulation …

• However, use of more accurate models may be costly in terms of computer resources and programming time

• Less accurate models have (very often) to be used in real life simulations

• Modeling errors can be made in– System modeling– Device modeling– Random process modeling– Processing

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Simulation …

– System modeling errors• Systems may include several elements and some of them (maybe

those believed to have an insignificant effect to the investigated behavior) are ignored from the simulation model

– Device modeling errors• Errors between the model and actual device

– Random process modeling errors• Real life signals are random ones (or include such components)• Errors to model actual random processes and errors to generate (in

computer) the modeled processes– Processing errors

• Due to limitations of computing medium and methodology• E.g., actual analog waveforms modeled by discrete signals in

computers (high over sampling rates, which may be used to model analog signals more precisely yield to increased simulation times)

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Simulation …• The models have to be validated (correctness check)

– A single block– A bunch of blocks– The overall system

• A possibility is to test them in a simplified environment where analytical results exist, and if the simulated results coincide with the analytical ones, the correctness has been “proved”, or, at least, some evidences about the correctness have been achieved

• Also the random process generator has to be validated

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Simulation …

• In summary, simulations are used– Since accurate analysis are very difficult or too time

consuming– To verify are simplified analyses accurate or

misleading– When one has to decide between different options

(parameters, algorithms, building blocks, …) which may have nonlinearities (= hard to analyze)

• Simulations are often much faster way to obtain the results

– To fasten and saving costs in production• Simulations are a tool in the design process!

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Simulation …

• In summary, in simulations– One has to program the model of the system and

its components (subsystems)• Existing simulation tools involve many prepared models

simplifying the programming• However, one has to validate the correctness of the

simulation program (if it s not already done by others)

– The modeling accuracy affects the simulation accuracy and also the efforts needed to make the simulation system

• Often simplified models are used• One has to be careful with simulation error sources and

validate correctness of his/her simulations

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Simulation …

• In summary, prepared simulation programs– Usually contain only necessary parts of the system

• Parts which are believed to have insignificant effects are ignored for simplicity (and to save efforts and time)

– Use a high-level model if possible (if their accuracy is sufficient)

• High-level model is e.g. a filter transfer function• Low-level models include details of (sub)systems

– E.g., build a filter model using models of actual elements used to build those filters

– Low-level modeling is time and effort consuming, and may not be necessary for the particular problem

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Simulation …

• Once more– Simulations are used to imitate reality– They may be used to check system’s performance

and expected behavior– However, the product (a mess of hardware and

software) may not perform like simulations (and analysis) predict. Why?

• Too simplified analysis & simulation models• Errors in modeling• Errors in simulation and analysis• Errors and/or incapability in producing

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Examples

• Examples of industry fields where simulations are used– Aerospace and defense– Communications– Automotive– Biotechnology– Medicine– Electronics– Financial modeling– Semiconductors

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Examples

• Aerospace and Defense– Every major aerospace and defense organization in

the world uses simulation products and services to develop air, naval, land, and space systems.

– Engineers and scientists rely on simulation tools for Model-Based Design and technical computing in programs such as the Airbus A380, F-35 Joint Strike Fighter, Mars Exploration Rover, as well as unmanned aerial vehicles and advanced wireless systems.

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Example: algorithm design Need for an algorithm to solve an existing real life

problem

receiving data in certain radio channels,recovering contaminated image, etc.

Simulation software is made

Blocks should model the reality at sufficientdetail/accuracy

Simulations are used to test how different algorithmsoperate at different expected use scenarios

Different channel parameters, different levels ofuncertainties is the received signals, different

contamination, etc.

The most promising (and implementable) algorithms areselected for prototyping (i.e., solving real life problem)

The real life problem is solved (by a prototype) and checked if thedeveloped algorithms can do what they should do

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A “serious” example

• E.g., you want to simulate your way from a bar to your home so that you can estimate how long it takes

– You have to think what things affect the model• Amount of alcohol, your capability to walk directly after certain amount of alcohol, ….• How you take your steps

– front, back and sides (what is the random process describing this)

• Are there enticements» Open bars on your route, a grill, irresistible persons (different/same sex depending on your tendency)

– You have to think how much time you want to spend to build the model• Will you make a perfect model (takes possibly a long time and a lot of efforts) • or do you ignore some effects that are too cumbersome to program

– and end up to a more easily made simulation model which possibly does not give as good results as the more accurate model

– After the simulator is ready, you will • run the simulations • compare simulation results to reality (if you could remember the reality)

– If simulations and reality coincide (within certain limits which you have to set) you are satisfied to your simulator, otherwise you have to improve your models (used inside the simulator)