Practice 2. Baseband Communication
|
|
- Cecily Stephens
- 5 years ago
- Views:
Transcription
1 PRACTICE : Practice. Baseband Communication.. Objectives To learn to use the software Simulink of MATLAB so as to analyze baseband communication systems... Practical development... Unipolar NRZ signal The program Simulink can be used to determine the main characteristics of a unipolar NRZ signal. Design a random generator for this kind of signals by using the block Bernoulli Binary Generator, available in the Communications Blockset (Comm Sources/Random Data Sources). Add a sample & hold circuit (Simulink/Discrete/ Hold) which will convert the data digits (ones or zeros) supplied by the binary generator in square waves with certain duration. In order to attain this, the parameter sample time of the block generator must be modified, which indicates the time between consecutive generated symbols. In this case, we are going to keep the default value of second. Net, set the sample time parameter of the block zero-order hold to the value, for eample, /6 s. We are going to add an channel (Communications Blockset/Channels/ Channel) to check the effect of noise over the transmitted signal. The mode of the channel will be set to signal to noise ratio (SNR), with a SNR value of db, for an input signal power (watts) of.5 W. This last value has not been established arbitrarily, but it is due to the input signal waveform. This signal is a binary wave with levels of volt or volts, each value being held during second. Then, if we consider that the probability of sing zeros or ones is the same and equal to.5, the mean power of this signal, the input signal to the channel, will be.5 W. B-FFT Spectrum Bernoulli Binary Bernoulli Binary Generator Hold Channel Discrete-Time Eye Diagram After that, we are going to introduce the visualization instruments into the system: a scope (Simulink/Sinks/), a spectrum scope (Signal Processing /7
2 PRACTICE : Blockset/Signal Processing Sinks/Spectrum ) and an eye diagram scope (Communications Blockset/Comm Sinks/Discrete-Time Eye Diagram ). In the scope, set two visualization aes (Parameters/number of aes) and deactivate Limit data points to last in Data History, so as to allow the block to save every sample received to its inputs. In the Spectrum, set Buffer size to 4, Buffer overlap to 5 and Number of spectral averages to 6. Finally, in the eye diagram scope, set samples per symbol to 6, offset (samples) to 8, i.e. 6/, and traces displayed to. In this last case, we are only indicating the number of samples per symbol, that is, 6, in the same way as it was established in the sample & hold circuit. In addition, the given offset value is required in order to centre the eye diagram in the screen, as we will see. Lastly, set the simulation time to, seconds.. Observe the eye diagram and check as the amplitude values of the signal swing approimately between and, plus a noise component. Also observe the square appearance of such eye diagrams.. Observe, in the scope, the unipolar signal with and without noise. 3. Observe the spectrum of the unipolar NRZ signal. By default, the spectrum is represented in decibels. In the properties of the spectrum scope, in the tab Ais Properties, set Frequency range to [-Fs/... Fs/], Amplitude scaling to Magnitude-squared, Minimum Y-Limit to a value of -5 and Maimum Y-Limit to. Run the simulation again and check the new appearance of the spectrum of the unipolar NRZ signal. Observe as there is a spectrum peak at Hz, which perfectly agrees with what is theoretically predicted. Moreover, the obtained spectrum has the shape of a sinc-squared function with nulls at n Hz, for n =,,..., etc.... Polar NRZ signal Net, we are going to analyze polar NRZ signals. To that, we are going to modify slightly the previous implemented system in order to remove the dc component from the unipolar signal and make the new generated signal take the values volt, that is: s ( s ( polar unipolar () Therefore, we only have to multiply by two the output signal from the generator (unipolar) and remove one to it, then obtaining the polar version of that same signal. The new system which allows us to analyze polar signals is shown in the net figure, where an analog filter has also been added (Signal Processing Blockset/Filtering/Filter Design/Analog Filter Design), as well as a system which calculates the mean power of any signal. /7
3 PRACTICE : B-FFT cheby Spectrum Bernoulli Binary Bernoulli Binary Generator Gain Hold Constant Analog Filter Design Hold Channel Discrete-Time Eye Diagram Running Var Variance In u Display Mean Math Function The mean power of a random signal is given by: P P AC P DC ( ( ( ( ( ( m ( () where indicates time averaging, as follows: T / f ( lim f ( dt (3) T T T / and where and m are the variance and the mean squared of, respectively. This is the reason why we have included the blocks to calculate the mean power of the signal. The blocks Variance and Mean are available in Signal Processing Blockset/Statistics. In the analogue filter, set Design method to Chebyshev II, keep the default values for Filter type, Filter order and Stopband attenuation in db, and set the cutoff frequency (Stopband edge frequency (rads/sec)) to.75 rad/s, i.e..75 Hz. With the last action, we are looking for distorting the signal in order to yield intersymbol interference (ISI) between consecutive symbols.. Set the simulation time to, seconds. Observe the eye diagram which is distorted by noise and the ISI induced by the channel filtering. In order to check the effect of the channel filtering over the spectrum of the received signal, set the cutoff frequency of the filter to rad/s during one simulation and then change this value to,75 rad/s in the net one. Observe how the secondary lobes and the highest frequencies of the main lobe are strongly attenuated by the filter. In order to better observe this changes, set Amplitude scaling in the tab Ais Properties of the spectrum scope to db. Check as the peak at Hz has also disappeared in this case for polar signals, which should not surprise us since this kind of 3/7
4 PRACTICE : signals present a null dc value (m = ). In fact, if we calculate its mean power, we would have: P polar m. Being established the filter cutoff frequency to,75 rad/s, remove the noise component by setting SNR to db in the properties of the block Channel. Moreover, set the input signal power to W (the polar signal has the double of mean power as the unipolar signal, which can be checked during the simulation by means of the display connected to the output of the different blocks whose task is to calculate the mean power). Check in the eye diagram the effect induced only by the ISI. In order to compare the obtained results with the ideal case (without filter), set the cutoff frequency to rad/s again and run a new simulation. Observe as the ISI, when we filter beyond.75 Hz, reduces the eye opening greatly. 3. Observe, by means of a scope, the shape of the temporal waves for the filtered and non-filtered signals. To that, connect the filter s input signal to the first scope input, and its output signal to the second scope input. 4. What do you think it would happen if we set the cutoff frequency to.6 Hz? And if we established it to.5 Hz?..3. Transmission using sinc-shaped pulses We are going to modify the shape of the pulse to that of sinc-shaped ones. To do this, in the previous system we will add several blocks whose task will be generating this kind of pulses. A sinc-shaped pulse is given by: f sen[ ( t / T] sinc[ ( t t )/ T] ( t t )/ T ( (4) where T is the elapsed time between consecutive nulls of the waveform and t is the time instant when the signal maimum is yielded. We are going to use a MATLAB function to generate this wave and add a block Simulink/User-Defined Functions/MATLAB Fcn which will be responsible of calling the previously defined MATLAB function (see net figure about the new system scheme). This block is inted to call a function, which we are going to denote as fsinc, with three input parameters (the input to the block, which is specified with letter u, the time duration of the pulse or elapsed time between consecutive nulls of the waveform, which we will set to second, and the number of samples per symbol time, which will be fied to 6). Thus, the parameter MATLAB function in the block properties must be set to fsinc(u,,6). Moreover, we have to establish the parameter Output dimensions to [6,], i.e. the output of the block will be a column vector of 6 rows. In order to convert this signal in a temporal 4/7
5 PRACTICE : sequence we have to introduce a block Frame Conversion followed by a block Unbuffer. In this way, the 6 output values from the block MATLAB Fcn will be converted to 6 temporal samples correctly ordered in time at a sample rate given by the block Hold placed net. B-FFT Bernoulli Binary Bernoulli Binary Generator Gain Constant MATLAB Function MATLAB Fcn [6] To Frame Frame Conversion [6] Unbuffer Hold cheby Analog Filter Design Hold Channel Spectrum Discrete-Time Eye Diagram The first thing we have to do is creating a function which generates the sincshaped waveform according to the range of values indicated as input. This MATLAB function can be easily defined in the net way: function y=sinc() % % Calculates sinc() for the range of values given by vector N=length(); y=zeros(,n); for i=:n if (i)== y(i)=; else y(i)=sin((i))/(i); The comments introduced below the function declaration will be epanded in the command window whenever we eecute the net instruction: help sinc Notice that the function sinc really eists as an internal function belonging to the MATLAB libraries. However, we have defined our own function in order to help us to better understand the implementation of the system we are describing. The function sinc() is characterized by presenting a maimum equal to one at =, and being null at values = n, where n is a natural number different to zero. Unlike rectangular pulses, where the pulse amplitude is only depent on the current data bit ( or ) along the T seconds of the symbol time, the sinc-shaped waveforms have ideally an infinite duration with non-negligible values for both negative and positive time instants. Theoretically, we cannot create waveforms with non-zero values at negative time instants, since the system would be not causal. However, we can take advantage of the amplitude attenuation of sinc shapes along the time. It can be checked as, beyond the 5th null after the maimum value, the amplitude has been attenuated greatly (is less than the % of the peak value of the signal). Notice that being strict we should consider greater elapsed times previous to make this kind of simplifications. Since the function sinc is symmetric from its 5/7
6 PRACTICE : peak point, we have to consider all the values both on the left and on the right previous to the 5th null from this peak value. In the net figure is depicted a sequence of binary data, the two first sinc waves corresponding to the first two data bits, as well the sum of these two waves (bottom graph). Evidently, to obtain the total waveform we have to add all the sinc waves corresponding to all the binary data. As an important remark, we can observe as the peak of the pulse waveform is delayed about 5 seconds (eactly 4.5 seconds) with respect to its corresponding binary bit. This is necessary to obtain a causal system, as we have previously indicated. The depicted signals also give us some information about the characteristics of the MATLAB function which we have to design to generate the sinc waveforms. In first place, we will have to save the values of the previous 9 bits because the time duration of the sinc pulse is 3T, being T the duration of each symbol or data bit (in this eample, a second). That is, in the eample shown in the figure, the 3 th bit has logic value and we would have to generate the sinc wave beginning from that instant, but we would also require from the previous 9 bits values whose contributions would be taking into account in the current time instant. Thus, the influence of the first bit would disappear after this time instant, and so on for the consecutive bits Net we show a possible MATLAB code to generate the sinc waveforms after receiving a new data bit: function signal_out=fsinc(new_data,t,n) persistent data; M=3; if isempty(data) data=zeros(,m); 6/7
7 PRACTICE : data=[data(:m) new_data]; signal_out=zeros(,n); t=:t/n:t*(-/n); for i=:m =pi*((t-t*(i-m/-.5))/t); signal_out=signal_out+data(i)*sinc(); signal_out=signal_out.'; In first place, we have declared a persistent variable in order to save its value whenever we go out of the function and call it again. This variable data saves the input binary data introduced to the block every T seconds, then it is set to zero the first time the function is called. Net, a new data bit is introduced to the block, new_data, which is apped to the memory data, removing the oldest one which was introduced 3 time instants before. Then the N-sample waveform is obtained during the current T seconds by adding the different contributions corresponding to the different data, both the new data bit and the previous ones: f ( M i [ t T( i M i sinc T /.5)], t T being i = according to the binary data value ( or ). The inde i goes from the first introduced data (i = ) to the current data bit (i = M ). Observe as this function only calculates the waveform during the present time interval, then requiring from saving previous data values along a long time. Finally, the generated waveform is saved in the variable signal_out, which is returned by the function and coincides with the output vector from block MATLAB Fcn.. By using the sinc-shaped pulses generator, check the waveforms in the time domain before and after the filter. What do you think about the good similarity between them?. Check the frequency spectrum of the sinc signal before and after the baseband filter with cutoff frequency of.75 Hz. What is approimately the bandwidth of the original sinc signal? 3. Check the effect of the ISI when we use sinc pulses. What main feature would you remark with respect to what was observed for rectangular pulses? 7/7
Experiment 2 Effects of Filtering
Experiment 2 Effects of Filtering INTRODUCTION This experiment demonstrates the relationship between the time and frequency domains. A basic rule of thumb is that the wider the bandwidth allowed for the
More informationExperiment 1 Introduction to MATLAB and Simulink
Experiment 1 Introduction to MATLAB and Simulink INTRODUCTION MATLAB s Simulink is a powerful modeling tool capable of simulating complex digital communications systems under realistic conditions. It includes
More informationEE3723 : Digital Communications
EE3723 : Digital Communications Week 11, 12: Inter Symbol Interference (ISI) Nyquist Criteria for ISI Pulse Shaping and Raised-Cosine Filter Eye Pattern Equalization (On Board) 01-Jun-15 Muhammad Ali Jinnah
More informationWireless Communication Systems Laboratory Lab#1: An introduction to basic digital baseband communication through MATLAB simulation Objective
Wireless Communication Systems Laboratory Lab#1: An introduction to basic digital baseband communication through MATLAB simulation Objective The objective is to teach students a basic digital communication
More informationEE5713 : Advanced Digital Communications
EE573 : Advanced Digital Communications Week 4, 5: Inter Symbol Interference (ISI) Nyquist Criteria for ISI Pulse Shaping and Raised-Cosine Filter Eye Pattern Error Performance Degradation (On Board) Demodulation
More informationQUESTION BANK SUBJECT: DIGITAL COMMUNICATION (15EC61)
QUESTION BANK SUBJECT: DIGITAL COMMUNICATION (15EC61) Module 1 1. Explain Digital communication system with a neat block diagram. 2. What are the differences between digital and analog communication systems?
More informationCOMMUNICATION LABORATORY
LAB 6: (PAM) PULSE AMPLITUDE MODULATION/DEMODULAT ION ON MATLAB/SIMULINK STUDENT NAME: STUDENT ID: SUBMISSION DATE : 15.04.2013 1/8 1. TECHNICAL BACKGROUND In pulse amplitude modulation, the amplitude
More informationEE390 Final Exam Fall Term 2002 Friday, December 13, 2002
Name Page 1 of 11 EE390 Final Exam Fall Term 2002 Friday, December 13, 2002 Notes 1. This is a 2 hour exam, starting at 9:00 am and ending at 11:00 am. The exam is worth a total of 50 marks, broken down
More informationENSC327 Communication Systems Fall 2011 Assignment #1 Due Wednesday, Sept. 28, 4:00 pm
ENSC327 Communication Systems Fall 2011 Assignment #1 Due Wednesday, Sept. 28, 4:00 pm All problem numbers below refer to those in Haykin & Moher s book. 1. (FT) Problem 2.20. 2. (Convolution) Problem
More informationLaboratory 5: Spread Spectrum Communications
Laboratory 5: Spread Spectrum Communications Cory J. Prust, Ph.D. Electrical Engineering and Computer Science Department Milwaukee School of Engineering Last Update: 19 September 2018 Contents 0 Laboratory
More informationModule 4. Signal Representation and Baseband Processing. Version 2 ECE IIT, Kharagpur
Module 4 Signal Representation and Baseband Processing Lesson 1 Nyquist Filtering and Inter Symbol Interference After reading this lesson, you will learn about: Power spectrum of a random binary sequence;
More informationSerial Data Transmission
Serial Data Transmission Dr. José Ernesto Rayas Sánchez 1 Outline Baseband serial transmission Line Codes Bandwidth of serial data streams Block codes Serialization Intersymbol Interference (ISI) Jitter
More informationECE 4600 Communication Systems
ECE 4600 Communication Systems Dr. Bradley J. Bazuin Associate Professor Department of Electrical and Computer Engineering College of Engineering and Applied Sciences Course Topics Course Introduction
More informationUNIT TEST I Digital Communication
Time: 1 Hour Class: T.E. I & II Max. Marks: 30 Q.1) (a) A compact disc (CD) records audio signals digitally by using PCM. Assume the audio signal B.W. to be 15 khz. (I) Find Nyquist rate. (II) If the Nyquist
More informationChapter 2 Channel Equalization
Chapter 2 Channel Equalization 2.1 Introduction In wireless communication systems signal experiences distortion due to fading [17]. As signal propagates, it follows multiple paths between transmitter and
More informationUltra Wideband Transceiver Design
Ultra Wideband Transceiver Design By: Wafula Wanjala George For: Bachelor Of Science In Electrical & Electronic Engineering University Of Nairobi SUPERVISOR: Dr. Vitalice Oduol EXAMINER: Dr. M.K. Gakuru
More informationSIGNALS AND SYSTEMS LABORATORY 13: Digital Communication
SIGNALS AND SYSTEMS LABORATORY 13: Digital Communication INTRODUCTION Digital Communication refers to the transmission of binary, or digital, information over analog channels. In this laboratory you will
More informationCommunications I (ELCN 306)
Communications I (ELCN 306) c Samy S. Soliman Electronics and Electrical Communications Engineering Department Cairo University, Egypt Email: samy.soliman@cu.edu.eg Website: http://scholar.cu.edu.eg/samysoliman
More informationLecture 3 Concepts for the Data Communications and Computer Interconnection
Lecture 3 Concepts for the Data Communications and Computer Interconnection Aim: overview of existing methods and techniques Terms used: -Data entities conveying meaning (of information) -Signals data
More informationSignals. Continuous valued or discrete valued Can the signal take any value or only discrete values?
Signals Continuous time or discrete time Is the signal continuous or sampled in time? Continuous valued or discrete valued Can the signal take any value or only discrete values? Deterministic versus random
More informationExploring QAM using LabView Simulation *
OpenStax-CNX module: m14499 1 Exploring QAM using LabView Simulation * Robert Kubichek This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 2.0 1 Exploring
More informationLab 8. Signal Analysis Using Matlab Simulink
E E 2 7 5 Lab June 30, 2006 Lab 8. Signal Analysis Using Matlab Simulink Introduction The Matlab Simulink software allows you to model digital signals, examine power spectra of digital signals, represent
More informationTSEK02: Radio Electronics Lecture 2: Modulation (I) Ted Johansson, EKS, ISY
TSEK02: Radio Electronics Lecture 2: Modulation (I) Ted Johansson, EKS, ISY 2 Basic Definitions Time and Frequency db conversion Power and dbm Filter Basics 3 Filter Filter is a component with frequency
More informationBasic Concepts in Data Transmission
Basic Concepts in Data Transmission EE450: Introduction to Computer Networks Professor A. Zahid A.Zahid-EE450 1 Data and Signals Data is an entity that convey information Analog Continuous values within
More informationSignal Processing for Digitizers
Signal Processing for Digitizers Modular digitizers allow accurate, high resolution data acquisition that can be quickly transferred to a host computer. Signal processing functions, applied in the digitizer
More informationTime division multiplexing The block diagram for TDM is illustrated as shown in the figure
CHAPTER 2 Syllabus: 1) Pulse amplitude modulation 2) TDM 3) Wave form coding techniques 4) PCM 5) Quantization noise and SNR 6) Robust quantization Pulse amplitude modulation In pulse amplitude modulation,
More informationLab 3.0. Pulse Shaping and Rayleigh Channel. Faculty of Information Engineering & Technology. The Communications Department
Faculty of Information Engineering & Technology The Communications Department Course: Advanced Communication Lab [COMM 1005] Lab 3.0 Pulse Shaping and Rayleigh Channel 1 TABLE OF CONTENTS 2 Summary...
More informationText Book: Simon Haykin & Michael Moher,
Qassim University College of Engineering Electrical Engineering Department Electronics and Communications Course: EE322 Digital Communications Prerequisite: EE320 Text Book: Simon Haykin & Michael Moher,
More informationLab course Analog Part of a State-of-the-Art Mobile Radio Receiver
Communication Technology Laboratory Wireless Communications Group Prof. Dr. A. Wittneben ETH Zurich, ETF, Sternwartstrasse 7, 8092 Zurich Tel 41 44 632 36 11 Fax 41 44 632 12 09 Lab course Analog Part
More information1/14. Signal. Surasak Sanguanpong Last updated: 11 July Signal 1/14
1/14 Signal Surasak Sanguanpong nguan@ku.ac.th http://www.cpe.ku.ac.th/~nguan Last updated: 11 July 2000 Signal 1/14 Transmission structure 2/14 Transmitter/ Receiver Medium Amplifier/ Repeater Medium
More informationFFT Analyzer. Gianfranco Miele, Ph.D
FFT Analyzer Gianfranco Miele, Ph.D www.eng.docente.unicas.it/gianfranco_miele g.miele@unicas.it Introduction It is a measurement instrument that evaluates the spectrum of a time domain signal applying
More informationEE 4440 Comm Theory Lab 5 Line Codes
EE 4440 Comm Theory Lab 5 Line Codes Purpose: The purpose of this lab is to investigate the properties of various line codes. Specific parameters investigated will be wave shape, bandwidth, and transparency.
More informationDigital Communication - Pulse Shaping
Digital Communication - Pulse Shaping After going through different types of coding techniques, we have an idea on how the data is prone to distortion and how the measures are taken to prevent it from
More informationExperiment 4 Detection of Antipodal Baseband Signals
Experiment 4 Detection of Antipodal Baseand Signals INRODUCION In previous experiments we have studied the transmission of data its as a 1 or a 0. hat is, a 1 volt signal represented the it value of 1
More informationIntroduction to Simulink
EE 460 Introduction to Communication Systems MATLAB Tutorial #3 Introduction to Simulink This tutorial provides an overview of Simulink. It also describes the use of the FFT Scope and the filter design
More informationSwedish College of Engineering and Technology Rahim Yar Khan
PRACTICAL WORK BOOK Telecommunication Systems and Applications (TL-424) Name: Roll No.: Batch: Semester: Department: Swedish College of Engineering and Technology Rahim Yar Khan Introduction Telecommunication
More informationTSEK02: Radio Electronics Lecture 2: Modulation (I) Ted Johansson, EKS, ISY
TSEK02: Radio Electronics Lecture 2: Modulation (I) Ted Johansson, EKS, ISY An Overview of Modulation Techniques: chapter 3.1 3.3.1 2 Introduction (3.1) Analog Modulation Amplitude Modulation Phase and
More informationData Communications & Computer Networks
Data Communications & Computer Networks Chapter 3 Data Transmission Fall 2008 Agenda Terminology and basic concepts Analog and Digital Data Transmission Transmission impairments Channel capacity Home Exercises
More informationHandout 11: Digital Baseband Transmission
ENGG 23-B: Principles of Communication Systems 27 8 First Term Handout : Digital Baseband Transmission Instructor: Wing-Kin Ma November 7, 27 Suggested Reading: Chapter 8 of Simon Haykin and Michael Moher,
More informationTSKS01 Digital Communication
Lab Memo for TSKS01 Digital Communication Mikael Olofsson Department of EE (ISY) Linköping University, SE-581 83 Linköping, Sweden Autumn 2010 Note: This lab memo is intended for the course TSKS01 Digital
More informationAnalyzing A/D and D/A converters
Analyzing A/D and D/A converters 2013. 10. 21. Pálfi Vilmos 1 Contents 1 Signals 3 1.1 Periodic signals 3 1.2 Sampling 4 1.2.1 Discrete Fourier transform... 4 1.2.2 Spectrum of sampled signals... 5 1.2.3
More informationEE 422G - Signals and Systems Laboratory
EE 422G - Signals and Systems Laboratory Lab 3 FIR Filters Written by Kevin D. Donohue Department of Electrical and Computer Engineering University of Kentucky Lexington, KY 40506 September 19, 2015 Objectives:
More informationData and Computer Communications Chapter 3 Data Transmission
Data and Computer Communications Chapter 3 Data Transmission Eighth Edition by William Stallings Transmission Terminology data transmission occurs between a transmitter & receiver via some medium guided
More informationDigital data (a sequence of binary bits) can be transmitted by various pule waveforms.
Chapter 2 Line Coding Digital data (a sequence of binary bits) can be transmitted by various pule waveforms. Sometimes these pulse waveforms have been called line codes. 2.1 Signalling Format Figure 2.1
More informationEE 215 Semester Project SPECTRAL ANALYSIS USING FOURIER TRANSFORM
EE 215 Semester Project SPECTRAL ANALYSIS USING FOURIER TRANSFORM Department of Electrical and Computer Engineering Missouri University of Science and Technology Page 1 Table of Contents Introduction...Page
More informationCHAPTER 4. PULSE MODULATION Part 2
CHAPTER 4 PULSE MODULATION Part 2 Pulse Modulation Analog pulse modulation: Sampling, i.e., information is transmitted only at discrete time instants. e.g. PAM, PPM and PDM Digital pulse modulation: Sampling
More informationAdvanced Digital Signal Processing Part 2: Digital Processing of Continuous-Time Signals
Advanced Digital Signal Processing Part 2: Digital Processing of Continuous-Time Signals Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Institute of Electrical Engineering
More informationECE 5650/4650 Exam II November 20, 2018 Name:
ECE 5650/4650 Exam II November 0, 08 Name: Take-Home Exam Honor Code This being a take-home exam a strict honor code is assumed. Each person is to do his/her own work. Bring any questions you have about
More informationCOSC 3213: Computer Networks I: Chapter 3 Handout #4. Instructor: Dr. Marvin Mandelbaum Department of Computer Science York University Section A
COSC 3213: Computer Networks I: Chapter 3 Handout #4 Instructor: Dr. Marvin Mandelbaum Department of Computer Science York University Section A Topics: 1. Line Coding: Unipolar, Polar,and Inverted ; Bipolar;
More informationIslamic University of Gaza. Faculty of Engineering Electrical Engineering Department Spring-2011
Islamic University of Gaza Faculty of Engineering Electrical Engineering Department Spring-2011 DSP Laboratory (EELE 4110) Lab#4 Sampling and Quantization OBJECTIVES: When you have completed this assignment,
More informationDigital Communication Systems Third year communications Midterm exam (15 points)
Name: Section: BN: Digital Communication Systems Third year communications Midterm exam (15 points) May 2011 Time: 1.5 hours 1- Determine if the following sentences are true of false (correct answer 0.5
More informationCALIFORNIA STATE UNIVERSITY, NORTHRIDGE FADING CHANNEL CHARACTERIZATION AND MODELING
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE FADING CHANNEL CHARACTERIZATION AND MODELING A graduate project submitted in partial fulfillment of the requirements For the degree of Master of Science in Electrical
More informationME scope Application Note 01 The FFT, Leakage, and Windowing
INTRODUCTION ME scope Application Note 01 The FFT, Leakage, and Windowing NOTE: The steps in this Application Note can be duplicated using any Package that includes the VES-3600 Advanced Signal Processing
More informationThe figures and the logic used for the MATLAB are given below.
MATLAB FIGURES & PROGRAM LOGIC: Transmitter: The figures and the logic used for the MATLAB are given below. Binary Data Sequence: For our project we assume that we have the digital binary data stream.
More informationLecture Fundamentals of Data and signals
IT-5301-3 Data Communications and Computer Networks Lecture 05-07 Fundamentals of Data and signals Lecture 05 - Roadmap Analog and Digital Data Analog Signals, Digital Signals Periodic and Aperiodic Signals
More informationPrinciples of Baseband Digital Data Transmission
Principles of Baseband Digital Data Transmission Prof. Wangrok Oh Dept. of Information Communications Eng. Chungnam National University Prof. Wangrok Oh(CNU) / 3 Overview Baseband Digital Data Transmission
More information17. Delta Modulation
7. Delta Modulation Introduction So far, we have seen that the pulse-code-modulation (PCM) technique converts analogue signals to digital format for transmission. For speech signals of 3.2kHz bandwidth,
More informationȘ.l. dr. ing. Lucian-Florentin Bărbulescu
Ș.l. dr. ing. Lucian-Florentin Bărbulescu 1 Data: entities that convey meaning within a computer system Signals: are the electric or electromagnetic impulses used to encode and transmit data Characteristics
More informationEC 554 Data Communications
EC 554 Data Communications Mohamed Khedr http://webmail. webmail.aast.edu/~khedraast.edu/~khedr Syllabus Tentatively Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week
More informationExperiment One: Generating Frequency Modulation (FM) Using Voltage Controlled Oscillator (VCO)
Experiment One: Generating Frequency Modulation (FM) Using Voltage Controlled Oscillator (VCO) Modified from original TIMS Manual experiment by Mr. Faisel Tubbal. Objectives 1) Learn about VCO and how
More information(i) Understanding of the characteristics of linear-phase finite impulse response (FIR) filters
FIR Filter Design Chapter Intended Learning Outcomes: (i) Understanding of the characteristics of linear-phase finite impulse response (FIR) filters (ii) Ability to design linear-phase FIR filters according
More informationExercise 3-2. Digital Modulation EXERCISE OBJECTIVE DISCUSSION OUTLINE DISCUSSION. PSK digital modulation
Exercise 3-2 Digital Modulation EXERCISE OBJECTIVE When you have completed this exercise, you will be familiar with PSK digital modulation and with a typical QPSK modulator and demodulator. DISCUSSION
More informationCHAPTER 3 Syllabus (2006 scheme syllabus) Differential pulse code modulation DPCM transmitter
CHAPTER 3 Syllabus 1) DPCM 2) DM 3) Base band shaping for data tranmission 4) Discrete PAM signals 5) Power spectra of discrete PAM signal. 6) Applications (2006 scheme syllabus) Differential pulse code
More informationDIGITAL COMMUNICATIONS SYSTEMS. MSc in Electronic Technologies and Communications
DIGITAL COMMUNICATIONS SYSTEMS MSc in Electronic Technologies and Communications Bandpass binary signalling The common techniques of bandpass binary signalling are: - On-off keying (OOK), also known as
More informationFund. of Digital Communications Ch. 3: Digital Modulation
Fund. of Digital Communications Ch. 3: Digital Modulation Klaus Witrisal witrisal@tugraz.at Signal Processing and Speech Communication Laboratory www.spsc.tugraz.at Graz University of Technology November
More informationEEE 309 Communication Theory
EEE 309 Communication Theory Semester: January 2016 Dr. Md. Farhad Hossain Associate Professor Department of EEE, BUET Email: mfarhadhossain@eee.buet.ac.bd Office: ECE 331, ECE Building Part 05 Pulse Code
More informationChapter 2 Direct-Sequence Systems
Chapter 2 Direct-Sequence Systems A spread-spectrum signal is one with an extra modulation that expands the signal bandwidth greatly beyond what is required by the underlying coded-data modulation. Spread-spectrum
More informationThus there are three basic modulation techniques: 1) AMPLITUDE SHIFT KEYING 2) FREQUENCY SHIFT KEYING 3) PHASE SHIFT KEYING
CHAPTER 5 Syllabus 1) Digital modulation formats 2) Coherent binary modulation techniques 3) Coherent Quadrature modulation techniques 4) Non coherent binary modulation techniques. Digital modulation formats:
More informationQiz 1. 3.discrete time signals can be obtained by a continuous-time signal. a. sampling b. digitizing c.defined d.
Qiz 1 Q1: 1.A periodic signal has a bandwidth of 20 Hz the highest frequency is 60Hz. what is the lowest frequency. a.20 b.40 c.60 d.30 2. find the value of bandwidth of the following signal S(t)=(1/5)
More informationJitter in Digital Communication Systems, Part 1
Application Note: HFAN-4.0.3 Rev.; 04/08 Jitter in Digital Communication Systems, Part [Some parts of this application note first appeared in Electronic Engineering Times on August 27, 200, Issue 8.] AVAILABLE
More informationIntroduction: Presence or absence of inherent error detection properties.
Introduction: Binary data can be transmitted using a number of different types of pulses. The choice of a particular pair of pulses to represent the symbols 1 and 0 is called Line Coding and the choice
More informationDigital Signal Processing. VO Embedded Systems Engineering Armin Wasicek WS 2009/10
Digital Signal Processing VO Embedded Systems Engineering Armin Wasicek WS 2009/10 Overview Signals and Systems Processing of Signals Display of Signals Digital Signal Processors Common Signal Processing
More information(i) Understanding of the characteristics of linear-phase finite impulse response (FIR) filters
FIR Filter Design Chapter Intended Learning Outcomes: (i) Understanding of the characteristics of linear-phase finite impulse response (FIR) filters (ii) Ability to design linear-phase FIR filters according
More informationLecture 13. Introduction to OFDM
Lecture 13 Introduction to OFDM Ref: About-OFDM.pdf Orthogonal frequency division multiplexing (OFDM) is well-known to be effective against multipath distortion. It is a multicarrier communication scheme,
More informationKeysight Technologies Pulsed Antenna Measurements Using PNA Network Analyzers
Keysight Technologies Pulsed Antenna Measurements Using PNA Network Analyzers White Paper Abstract This paper presents advances in the instrumentation techniques that can be used for the measurement and
More informationChapter Two. Fundamentals of Data and Signals. Data Communications and Computer Networks: A Business User's Approach Seventh Edition
Chapter Two Fundamentals of Data and Signals Data Communications and Computer Networks: A Business User's Approach Seventh Edition After reading this chapter, you should be able to: Distinguish between
More informationData and Computer Communications. Chapter 3 Data Transmission
Data and Computer Communications Chapter 3 Data Transmission Data Transmission quality of the signal being transmitted The successful transmission of data depends on two factors: characteristics of the
More informationDIGITAL CPFSK TRANSMITTER AND NONCOHERENT RECEIVER/DEMODULATOR IMPLEMENTATION 1
DIGIAL CPFSK RANSMIER AND NONCOHEREN RECEIVER/DEMODULAOR IMPLEMENAION 1 Eric S. Otto and Phillip L. De León New Meico State University Center for Space elemetry and elecommunications ABSRAC As radio frequency
More informationData Communication. Chapter 3 Data Transmission
Data Communication Chapter 3 Data Transmission ١ Terminology (1) Transmitter Receiver Medium Guided medium e.g. twisted pair, coaxial cable, optical fiber Unguided medium e.g. air, water, vacuum ٢ Terminology
More informationEXPERIMENT 4 INTRODUCTION TO AMPLITUDE MODULATION SUBMITTED BY
EXPERIMENT 4 INTRODUCTION TO AMPLITUDE MODULATION SUBMITTED BY NAME:. STUDENT ID:.. ROOM: INTRODUCTION TO AMPLITUDE MODULATION Purpose: The objectives of this laboratory are:. To introduce the spectrum
More informationFROM BLIND SOURCE SEPARATION TO BLIND SOURCE CANCELLATION IN THE UNDERDETERMINED CASE: A NEW APPROACH BASED ON TIME-FREQUENCY ANALYSIS
' FROM BLIND SOURCE SEPARATION TO BLIND SOURCE CANCELLATION IN THE UNDERDETERMINED CASE: A NEW APPROACH BASED ON TIME-FREQUENCY ANALYSIS Frédéric Abrard and Yannick Deville Laboratoire d Acoustique, de
More informationContents. Introduction 1 1 Suggested Reading 2 2 Equipment and Software Tools 2 3 Experiment 2
ECE363, Experiment 02, 2018 Communications Lab, University of Toronto Experiment 02: Noise Bruno Korst - bkf@comm.utoronto.ca Abstract This experiment will introduce you to some of the characteristics
More informationECEGR Lab #8: Introduction to Simulink
Page 1 ECEGR 317 - Lab #8: Introduction to Simulink Objective: By: Joe McMichael This lab is an introduction to Simulink. The student will become familiar with the Help menu, go through a short example,
More informationDesign of FIR Filter for Efficient Utilization of Speech Signal Akanksha. Raj 1 Arshiyanaz. Khateeb 2 Fakrunnisa.Balaganur 3
IJSRD - International Journal for Scientific Research & Development Vol. 3, Issue 03, 2015 ISSN (online): 2321-0613 Design of FIR Filter for Efficient Utilization of Speech Signal Akanksha. Raj 1 Arshiyanaz.
More informationComm 502: Communication Theory
Comm 50: Communication Theory Prof. Dean of the faculty of IET The German University in Cairo 1 COMM 50: Communication Theory Instructor: Ahmed El-Mahdy Office : C3.319 Lecture Time: Sat. nd Slot Office
More informationHello and welcome to today s lecture. In the last couple of lectures we have discussed about various transmission media.
Data Communication Prof. Ajit Pal Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur Lecture No # 7 Transmission of Digital Signal-I Hello and welcome to today s lecture.
More informationExperiment Five: The Noisy Channel Model
Experiment Five: The Noisy Channel Model Modified from original TIMS Manual experiment by Mr. Faisel Tubbal. Objectives 1) Study and understand the use of marco CHANNEL MODEL module to generate and add
More informationLab 3 SPECTRUM ANALYSIS OF THE PERIODIC RECTANGULAR AND TRIANGULAR SIGNALS 3.A. OBJECTIVES 3.B. THEORY
Lab 3 SPECRUM ANALYSIS OF HE PERIODIC RECANGULAR AND RIANGULAR SIGNALS 3.A. OBJECIVES. he spectrum of the periodic rectangular and triangular signals.. he rejection of some harmonics in the spectrum of
More informationCHAPTER 3 ADAPTIVE MODULATION TECHNIQUE WITH CFO CORRECTION FOR OFDM SYSTEMS
44 CHAPTER 3 ADAPTIVE MODULATION TECHNIQUE WITH CFO CORRECTION FOR OFDM SYSTEMS 3.1 INTRODUCTION A unique feature of the OFDM communication scheme is that, due to the IFFT at the transmitter and the FFT
More informationChapter 3 Data Transmission COSC 3213 Summer 2003
Chapter 3 Data Transmission COSC 3213 Summer 2003 Courtesy of Prof. Amir Asif Definitions 1. Recall that the lowest layer in OSI is the physical layer. The physical layer deals with the transfer of raw
More informationNoise Measurements Using a Teledyne LeCroy Oscilloscope
Noise Measurements Using a Teledyne LeCroy Oscilloscope TECHNICAL BRIEF January 9, 2013 Summary Random noise arises from every electronic component comprising your circuits. The analysis of random electrical
More informationFundamentals of Data and Signals
Fundamentals of Data and Signals Chapter 2 Learning Objectives After reading this chapter, you should be able to: Distinguish between data and signals and cite the advantages of digital data and signals
More informationHandout 13: Intersymbol Interference
ENGG 2310-B: Principles of Communication Systems 2018 19 First Term Handout 13: Intersymbol Interference Instructor: Wing-Kin Ma November 19, 2018 Suggested Reading: Chapter 8 of Simon Haykin and Michael
More informationChapter 3 Data Transmission
Chapter 3 Data Transmission COSC 3213 Instructor: U.T. Nguyen 1 9/27/2007 3:21 PM Terminology (1) Transmitter Receiver Medium Guided medium e.g. twisted pair, optical fiber Unguided medium e.g. air, water,
More informationLab 1B LabVIEW Filter Signal
Lab 1B LabVIEW Filter Signal Due Thursday, September 12, 2013 Submit Responses to Questions (Hardcopy) Equipment: LabVIEW Setup: Open LabVIEW Skills learned: Create a low- pass filter using LabVIEW and
More informationOFDM system: Discrete model Spectral efficiency Characteristics. OFDM based multiple access schemes. OFDM sensitivity to synchronization errors
Introduction - Motivation OFDM system: Discrete model Spectral efficiency Characteristics OFDM based multiple access schemes OFDM sensitivity to synchronization errors 4 OFDM system Main idea: to divide
More informationEEE482F: Problem Set 1
EEE482F: Problem Set 1 1. A digital source emits 1.0 and 0.0V levels with a probability of 0.2 each, and +3.0 and +4.0V levels with a probability of 0.3 each. Evaluate the average information of the source.
More informationData Communications and Networks
Data Communications and Networks Abdul-Rahman Mahmood http://alphapeeler.sourceforge.net http://pk.linkedin.com/in/armahmood abdulmahmood-sss twitter.com/alphapeeler alphapeeler.sourceforge.net/pubkeys/pkey.htm
More informationProblem Sheet 1 Probability, random processes, and noise
Problem Sheet 1 Probability, random processes, and noise 1. If F X (x) is the distribution function of a random variable X and x 1 x 2, show that F X (x 1 ) F X (x 2 ). 2. Use the definition of the cumulative
More informationWaveshaping Synthesis. Indexing. Waveshaper. CMPT 468: Waveshaping Synthesis
Waveshaping Synthesis CMPT 468: Waveshaping Synthesis Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University October 8, 23 In waveshaping, it is possible to change the spectrum
More information