Digital Communication Systems Master of Electrical Engineering and Informaiton Technology. Lecture 4 : Channel Models and Channel Capacity
|
|
- Camron Lindsey
- 7 years ago
- Views:
Transcription
1 Digital Communication Systems Master of Electrical Engineering and Informaiton Technology Lecture 4 : Channel Models and Channel Capacity
2 Channel Models and Channel Capacity Ref: Digital Communication Systems by J.G. Proakis Channel Capacity is defined for any communication channel and gives a fundamental limit on the amount of information that can be limited to through the channel Types of Channels Binary Symmetric Channel (BSC) Additive white Gaussian Noise (AWGN) Channels
3 Channel Models and Channel Capacity Transmitting the information signal is subject to a variety of changes deterministic and probabilistic e.g. Addition of noise, multi-path fading etc. The mathematical model for a communication channel is stochastic dependence between the input and the output signal Channel can be modeled as a conditional probability relating each output of the channel to is corresponding Input Genral model is called Discrete Memory less Channel (DMC) Input Alphabet x Output Alphabet Y Channel Transition Probability p(y X) for all x Ξ X, y Ξ Y
4 Specific forms of the DMC Binary Symmetric Channel (BSC) Binary Eraser Channel (BEC) p p -p p q -p e Error, erased -p -p Binary Channel (BC) Binary Eraser & Error Channel (BE&EC) -p p q - p - p 2 p 2 p q q 2 e -q - q - q 2
5 DMC with feedback X i DMC Y i feedback If feedback is available to the Tx then its a feedback channel With feedback you can not transmit more information compared to that without feedback Decrete Channel with feedback use previous information to transmit the future symbols capacity of the feedback channel does not increase If channel has memory you can transmit possibly more informaiton Continuous & Discrete Valued Channel Transmits Continuous/Discrete information AWGN Channel Noise is added and is White X i Y i =X i +Z i X(t) Continuous Discrete X Z(t) Z Y(t) =X(t) + Z(t) Y=X+Z Z i Autocorrelation fn. of noise is derac delta fn. Guassian with PSD N /2 Channel is memory less PSD N /2
6 Additive Gaussian Noise (AGN) Channel with memory Transmits Continuous/Discrete information with memory Continuous Noise Z and i Z(t) are added but NOT White, although correlated in time Discrete Autocorrelation fn. of noise is NOT FLAT in FD There is a correlation in noise in time domain Channel is has a memory Linear Filter Channel(LF Ch) with memory X(t) Continuous If g i is the IR of the inverse filter of h i at reciever Discrete Then we can simply pass the signal through g i X i Y h i i g i Y i X(t) X i h(t) h(t) finite/infinite IR Z(t) Z(t) Z i Y(t) =X(t) + Z(t) Y i =X i +Z i Z i, Z(t) Correlated Gaussian Noise Z(t) White Gaussian Noise Y(t) =X(t) X h(t) +Z(t) X i Y =X X i i h i +Z i h i h i finite/infinite IR Z i Z i White Gaussian Noise Zi If we consider dotted box as a channel then LFCh can be shown as Y i = g X i Y i = (X X i h i +Z i ) X g i = X X i (h g i i ) + Z X X i g i (h g i X i ) convolution of h i & g i is delta fn. = X X i (delta fn.) + Z X i g i X i = X i + Z X i g i X (delta fn.) = X i X i Z i X g i But noise is not white, it is colored noise AGN Ch with colored noise can be converted to LF Ch by use of whitening filter, which makes the noise white Y i X i Z i g i Z i Colored Gaussian Noise Z i White Gaussian Noise Y i =X X g i i + Z i Z i = Z X g i i AGN Ch and LFCh are equivalent and convertible by use of whitening filter
7 Binary Symmetric Channel (BSC) Special case of DMC A mathematical model for binary transmission over a Guassian Channel with hard decision at the output. X = y = [,] and p (y= x=) = (y= x=) =e e is called the crossover probability of the channel Channel Capacity C Is the maximum rate at which reliable transmission of information over the channel is possible Conditions for reliable transmission There exists a sequence of codes with increasing block length ofr which the errror probability tends to as the block lenght increases At rates R<C reliable transmission over the channel is possible, R>C reliable transmission over the channel is not possible
8 Channel Capacity for DMC Shannon s fundamental result of information theory states where denotes the Mutual Informaiton between x (ch I/P) and y (ch O/P) Mutual Information between two r.v. the maximization is carried out over all input probability distributions of the channel Capacity of BSC In bits and the logrithm is in base 2. Binary Entropy H b (.) where Ξ is the cross probability of the channe Hb(.) is Binary Entropy
9 The Bandlimited Additive White Gaussian Noise Channel with an input power constraint Channel is and limited to [-w,w] Noise is Guassian and White Power Spectral density of N/2 bits/ second For a discrete time Additivie White Gaussian Noise Channel with input power constraint Noise variance bits/ transmission
10 Ex:Capacity of the BSC Binary data AWGN BPSK signaling Optimal matched filter detection Hard decision decoding Plot Error probability of the channel as a function of Energy in each BPSK signal N/2 Noise Power Spectral density Assume changes from -2 db to 2dB Plot resulting channel a function of Error probability of BPSK with optimal detection is given by bits/ second Use relation to obtain a plot between C verus
11 The Q-Function and Error Function Q-Function Q(x) is the probability that a standard normal r.v. (t) will obtain a value larger than x. t 2 dt Its a simple transformations of the normal CDF which assumes a value in the range [,x] The Q-function can be expressed in terms of the Error Function as Q(x) X The Error Function ( erf(x) ) or Gauss error function is a function of sigmoid shape The Complementary Error Function, erfc(x), is defined as erf(x) X
12 Ex: Gaussian Channel Capacity. Plot the capacity of an additive white Gaussian noise channel with a bandwidth W= 3 Hz as a function P/N o for values of P/No between -2 an d 3 db 2. Plot the capacity of an additive white Gaussian noise channel with P/No = 25 db as a function of W. In particular, what is the channel capacity when W increases indefinitely? As in seen in the plots, when either P/No or W tend to zero, the capacity of the channel also tends to zero when P/No or W tends to infinity, the capacity behaves differently. when P/No tends to infinity, the capacity tends to infinity when W tends to infinity, the capacity goes to a limit determined by P/No
13 Ex: Capacity of the Binary input AWGN Channel A binary input AWGN channel is modeled by two binary i/p levels A & -A and additive (zero mean) Gaussian noise with variance In this case x= {A, -A }, Plot the capcity of this channel as a function of Due to symmetry in this problem the capacity is achieved for uniform input distribution i.e., for For this input distribution the output distribution is given by and the mutual information between the input and th eoutput is given by Simple integration and change of variable results in where Using these relaitons we can calculate I(X;Y) for vaious values of
14 Ex: Capacity of the bandlimited AWGN Channel with input power P and Bandwidth W Capacity a descrete time AWGN channel as a function as a function of BW and SNR in AWGN channel
15
16
17
18
19 xed
Probability and Random Variables. Generation of random variables (r.v.)
Probability and Random Variables Method for generating random variables with a specified probability distribution function. Gaussian And Markov Processes Characterization of Stationary Random Process Linearly
More informationMIMO CHANNEL CAPACITY
MIMO CHANNEL CAPACITY Ochi Laboratory Nguyen Dang Khoa (D1) 1 Contents Introduction Review of information theory Fixed MIMO channel Fading MIMO channel Summary and Conclusions 2 1. Introduction The use
More informationPHASE ESTIMATION ALGORITHM FOR FREQUENCY HOPPED BINARY PSK AND DPSK WAVEFORMS WITH SMALL NUMBER OF REFERENCE SYMBOLS
PHASE ESTIMATION ALGORITHM FOR FREQUENCY HOPPED BINARY PSK AND DPSK WAVEFORMS WITH SMALL NUM OF REFERENCE SYMBOLS Benjamin R. Wiederholt The MITRE Corporation Bedford, MA and Mario A. Blanco The MITRE
More informationChapter 1 Introduction
Chapter 1 Introduction 1. Shannon s Information Theory 2. Source Coding theorem 3. Channel Coding Theory 4. Information Capacity Theorem 5. Introduction to Error Control Coding Appendix A : Historical
More information5 Signal Design for Bandlimited Channels
225 5 Signal Design for Bandlimited Channels So far, we have not imposed any bandwidth constraints on the transmitted passband signal, or equivalently, on the transmitted baseband signal s b (t) I[k]g
More informationDigital Baseband Modulation
Digital Baseband Modulation Later Outline Baseband & Bandpass Waveforms Baseband & Bandpass Waveforms, Modulation A Communication System Dig. Baseband Modulators (Line Coders) Sequence of bits are modulated
More informationCapacity Limits of MIMO Channels
Tutorial and 4G Systems Capacity Limits of MIMO Channels Markku Juntti Contents 1. Introduction. Review of information theory 3. Fixed MIMO channels 4. Fading MIMO channels 5. Summary and Conclusions References
More informationDepartment of Electrical and Computer Engineering Ben-Gurion University of the Negev. LAB 1 - Introduction to USRP
Department of Electrical and Computer Engineering Ben-Gurion University of the Negev LAB 1 - Introduction to USRP - 1-1 Introduction In this lab you will use software reconfigurable RF hardware from National
More informationDigital Modulation. David Tipper. Department of Information Science and Telecommunications University of Pittsburgh. Typical Communication System
Digital Modulation David Tipper Associate Professor Department of Information Science and Telecommunications University of Pittsburgh http://www.tele.pitt.edu/tipper.html Typical Communication System Source
More informationCoding and decoding with convolutional codes. The Viterbi Algor
Coding and decoding with convolutional codes. The Viterbi Algorithm. 8 Block codes: main ideas Principles st point of view: infinite length block code nd point of view: convolutions Some examples Repetition
More informationLezione 6 Communications Blockset
Corso di Tecniche CAD per le Telecomunicazioni A.A. 2007-2008 Lezione 6 Communications Blockset Ing. Marco GALEAZZI 1 What Is Communications Blockset? Communications Blockset extends Simulink with a comprehensive
More informationSymbol interval T=1/(2B); symbol rate = 1/T=2B transmissions/sec (The transmitted baseband signal is assumed to be real here) Noise power = (N_0/2)(2B)=N_0B \Gamma is no smaller than 1 The encoded PAM
More informationELEC3028 Digital Transmission Overview & Information Theory. Example 1
Example. A source emits symbols i, i 6, in the BCD format with probabilities P( i ) as given in Table, at a rate R s = 9.6 kbaud (baud=symbol/second). State (i) the information rate and (ii) the data rate
More informationMODULATION Systems (part 1)
Technologies and Services on Digital Broadcasting (8) MODULATION Systems (part ) "Technologies and Services of Digital Broadcasting" (in Japanese, ISBN4-339-62-2) is published by CORONA publishing co.,
More informationDigital Transmission (Line Coding)
Digital Transmission (Line Coding) Pulse Transmission Source Multiplexer Line Coder Line Coding: Output of the multiplexer (TDM) is coded into electrical pulses or waveforms for the purpose of transmission
More informationSignal Detection C H A P T E R 14 14.1 SIGNAL DETECTION AS HYPOTHESIS TESTING
C H A P T E R 4 Signal Detection 4. SIGNAL DETECTION AS HYPOTHESIS TESTING In Chapter 3 we considered hypothesis testing in the context of random variables. The detector resulting in the minimum probability
More informationChapter 8 - Power Density Spectrum
EE385 Class Notes 8/8/03 John Stensby Chapter 8 - Power Density Spectrum Let X(t) be a WSS random process. X(t) has an average power, given in watts, of E[X(t) ], a constant. his total average power is
More informationADVANCED APPLICATIONS OF ELECTRICAL ENGINEERING
Development of a Software Tool for Performance Evaluation of MIMO OFDM Alamouti using a didactical Approach as a Educational and Research support in Wireless Communications JOSE CORDOVA, REBECA ESTRADA
More informationPerformance of Quasi-Constant Envelope Phase Modulation through Nonlinear Radio Channels
Performance of Quasi-Constant Envelope Phase Modulation through Nonlinear Radio Channels Qi Lu, Qingchong Liu Electrical and Systems Engineering Department Oakland University Rochester, MI 48309 USA E-mail:
More informationConvolution, Correlation, & Fourier Transforms. James R. Graham 10/25/2005
Convolution, Correlation, & Fourier Transforms James R. Graham 10/25/2005 Introduction A large class of signal processing techniques fall under the category of Fourier transform methods These methods fall
More informationNRZ Bandwidth - HF Cutoff vs. SNR
Application Note: HFAN-09.0. Rev.2; 04/08 NRZ Bandwidth - HF Cutoff vs. SNR Functional Diagrams Pin Configurations appear at end of data sheet. Functional Diagrams continued at end of data sheet. UCSP
More informationTTT4110 Information and Signal Theory Solution to exam
Norwegian University of Science and Technology Department of Electronics and Telecommunications TTT4 Information and Signal Theory Solution to exam Problem I (a The frequency response is found by taking
More informationBER Performance Analysis of SSB-QPSK over AWGN and Rayleigh Channel
Performance Analysis of SSB-QPSK over AWGN and Rayleigh Channel Rahul Taware ME Student EXTC Department, DJSCOE Vile-Parle (W) Mumbai 056 T. D Biradar Associate Professor EXTC Department, DJSCOE Vile-Parle
More informationPolarization codes and the rate of polarization
Polarization codes and the rate of polarization Erdal Arıkan, Emre Telatar Bilkent U., EPFL Sept 10, 2008 Channel Polarization Given a binary input DMC W, i.i.d. uniformly distributed inputs (X 1,...,
More informationTCOM 370 NOTES 99-4 BANDWIDTH, FREQUENCY RESPONSE, AND CAPACITY OF COMMUNICATION LINKS
TCOM 370 NOTES 99-4 BANDWIDTH, FREQUENCY RESPONSE, AND CAPACITY OF COMMUNICATION LINKS 1. Bandwidth: The bandwidth of a communication link, or in general any system, was loosely defined as the width of
More informationELE745 Assignment and Lab Manual
ELE745 Assignment and Lab Manual August 22, 2010 CONTENTS 1. Assignment 1........................................ 1 1.1 Assignment 1 Problems................................ 1 1.2 Assignment 1 Solutions................................
More informationFUNDAMENTALS of INFORMATION THEORY and CODING DESIGN
DISCRETE "ICS AND ITS APPLICATIONS Series Editor KENNETH H. ROSEN FUNDAMENTALS of INFORMATION THEORY and CODING DESIGN Roberto Togneri Christopher J.S. desilva CHAPMAN & HALL/CRC A CRC Press Company Boca
More informationBSEE Degree Plan Bachelor of Science in Electrical Engineering: 2015-16
BSEE Degree Plan Bachelor of Science in Electrical Engineering: 2015-16 Freshman Year ENG 1003 Composition I 3 ENG 1013 Composition II 3 ENGR 1402 Concepts of Engineering 2 PHYS 2034 University Physics
More informationVoice---is analog in character and moves in the form of waves. 3-important wave-characteristics:
Voice Transmission --Basic Concepts-- Voice---is analog in character and moves in the form of waves. 3-important wave-characteristics: Amplitude Frequency Phase Voice Digitization in the POTS Traditional
More informationMaximal Capacity Partial Response Signaling
Maximal Capacity Partial Response Signaling Fredrik Rusek and John B. Anderson Dept. of Information echnology Lund University, Lund, Sweden Email: {fredrikr,anderson}@it.lth.se Abstract In this paper we
More informationContents. A Error probability for PAM Signals in AWGN 17. B Error probability for PSK Signals in AWGN 18
Contents 5 Signal Constellations 3 5.1 Pulse-amplitude Modulation (PAM).................. 3 5.1.1 Performance of PAM in Additive White Gaussian Noise... 4 5.2 Phase-shift Keying............................
More informationApplication Note Noise Frequently Asked Questions
: What is? is a random signal inherent in all physical components. It directly limits the detection and processing of all information. The common form of noise is white Gaussian due to the many random
More informationAdvanced Signal Processing and Digital Noise Reduction
Advanced Signal Processing and Digital Noise Reduction Saeed V. Vaseghi Queen's University of Belfast UK WILEY HTEUBNER A Partnership between John Wiley & Sons and B. G. Teubner Publishers Chichester New
More informationSolutions to Exam in Speech Signal Processing EN2300
Solutions to Exam in Speech Signal Processing EN23 Date: Thursday, Dec 2, 8: 3: Place: Allowed: Grades: Language: Solutions: Q34, Q36 Beta Math Handbook (or corresponding), calculator with empty memory.
More informationOn the Traffic Capacity of Cellular Data Networks. 1 Introduction. T. Bonald 1,2, A. Proutière 1,2
On the Traffic Capacity of Cellular Data Networks T. Bonald 1,2, A. Proutière 1,2 1 France Telecom Division R&D, 38-40 rue du Général Leclerc, 92794 Issy-les-Moulineaux, France {thomas.bonald, alexandre.proutiere}@francetelecom.com
More informationTime and Frequency Domain Equalization
Time and Frequency Domain Equalization Presented By: Khaled Shawky Hassan Under Supervision of: Prof. Werner Henkel Introduction to Equalization Non-ideal analog-media such as telephone cables and radio
More informationAdaptive Equalization of binary encoded signals Using LMS Algorithm
SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) volume issue7 Sep Adaptive Equalization of binary encoded signals Using LMS Algorithm Dr.K.Nagi Reddy Professor of ECE,NBKR
More informationIntroduction to Probability
Introduction to Probability EE 179, Lecture 15, Handout #24 Probability theory gives a mathematical characterization for experiments with random outcomes. coin toss life of lightbulb binary data sequence
More informationLog-Likelihood Ratio-based Relay Selection Algorithm in Wireless Network
Recent Advances in Electrical Engineering and Electronic Devices Log-Likelihood Ratio-based Relay Selection Algorithm in Wireless Network Ahmed El-Mahdy and Ahmed Walid Faculty of Information Engineering
More informationVector Spaces; the Space R n
Vector Spaces; the Space R n Vector Spaces A vector space (over the real numbers) is a set V of mathematical entities, called vectors, U, V, W, etc, in which an addition operation + is defined and in which
More informationRevision of Lecture Eighteen
Revision of Lecture Eighteen Previous lecture has discussed equalisation using Viterbi algorithm: Note similarity with channel decoding using maximum likelihood sequence estimation principle It also discusses
More informationTeaching Convolutional Coding using MATLAB in Communication Systems Course. Abstract
Section T3C2 Teaching Convolutional Coding using MATLAB in Communication Systems Course Davoud Arasteh Department of Electronic Engineering Technology, LA 70813, USA Abstract Convolutional codes are channel
More informationNon-Data Aided Carrier Offset Compensation for SDR Implementation
Non-Data Aided Carrier Offset Compensation for SDR Implementation Anders Riis Jensen 1, Niels Terp Kjeldgaard Jørgensen 1 Kim Laugesen 1, Yannick Le Moullec 1,2 1 Department of Electronic Systems, 2 Center
More informationCommunication on the Grassmann Manifold: A Geometric Approach to the Noncoherent Multiple-Antenna Channel
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 48, NO. 2, FEBRUARY 2002 359 Communication on the Grassmann Manifold: A Geometric Approach to the Noncoherent Multiple-Antenna Channel Lizhong Zheng, Student
More informationIEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 55, NO. 1, JANUARY 2007 341
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 55, NO. 1, JANUARY 2007 341 Multinode Cooperative Communications in Wireless Networks Ahmed K. Sadek, Student Member, IEEE, Weifeng Su, Member, IEEE, and K.
More informationImplementation of Digital Signal Processing: Some Background on GFSK Modulation
Implementation of Digital Signal Processing: Some Background on GFSK Modulation Sabih H. Gerez University of Twente, Department of Electrical Engineering s.h.gerez@utwente.nl Version 4 (February 7, 2013)
More informationSphere-Bound-Achieving Coset Codes and Multilevel Coset Codes
820 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 46, NO 3, MAY 2000 Sphere-Bound-Achieving Coset Codes and Multilevel Coset Codes G David Forney, Jr, Fellow, IEEE, Mitchell D Trott, Member, IEEE, and Sae-Young
More informationDigital Transmission of Analog Data: PCM and Delta Modulation
Digital Transmission of Analog Data: PCM and Delta Modulation Required reading: Garcia 3.3.2 and 3.3.3 CSE 323, Fall 200 Instructor: N. Vlajic Digital Transmission of Analog Data 2 Digitization process
More informationLecture 8: Signal Detection and Noise Assumption
ECE 83 Fall Statistical Signal Processing instructor: R. Nowak, scribe: Feng Ju Lecture 8: Signal Detection and Noise Assumption Signal Detection : X = W H : X = S + W where W N(, σ I n n and S = [s, s,...,
More informationPower Spectral Density
C H A P E R 0 Power Spectral Density INRODUCION Understanding how the strength of a signal is distributed in the frequency domain, relative to the strengths of other ambient signals, is central to the
More informationKhalid Sayood and Martin C. Rost Department of Electrical Engineering University of Nebraska
PROBLEM STATEMENT A ROBUST COMPRESSION SYSTEM FOR LOW BIT RATE TELEMETRY - TEST RESULTS WITH LUNAR DATA Khalid Sayood and Martin C. Rost Department of Electrical Engineering University of Nebraska The
More informationCapacity Limits of MIMO Systems
1 Capacity Limits of MIMO Systems Andrea Goldsmith, Syed Ali Jafar, Nihar Jindal, and Sriram Vishwanath 2 I. INTRODUCTION In this chapter we consider the Shannon capacity limits of single-user and multi-user
More informationT = 1 f. Phase. Measure of relative position in time within a single period of a signal For a periodic signal f(t), phase is fractional part t p
Data Transmission Concepts and terminology Transmission terminology Transmission from transmitter to receiver goes over some transmission medium using electromagnetic waves Guided media. Waves are guided
More informationMIMO: What shall we do with all these degrees of freedom?
MIMO: What shall we do with all these degrees of freedom? Helmut Bölcskei Communication Technology Laboratory, ETH Zurich June 4, 2003 c H. Bölcskei, Communication Theory Group 1 Attributes of Future Broadband
More informationController Design in Frequency Domain
ECSE 4440 Control System Engineering Fall 2001 Project 3 Controller Design in Frequency Domain TA 1. Abstract 2. Introduction 3. Controller design in Frequency domain 4. Experiment 5. Colclusion 1. Abstract
More informationComparison of Network Coding and Non-Network Coding Schemes for Multi-hop Wireless Networks
Comparison of Network Coding and Non-Network Coding Schemes for Multi-hop Wireless Networks Jia-Qi Jin, Tracey Ho California Institute of Technology Pasadena, CA Email: {jin,tho}@caltech.edu Harish Viswanathan
More informationEE 179 April 21, 2014 Digital and Analog Communication Systems Handout #16 Homework #2 Solutions
EE 79 April, 04 Digital and Analog Communication Systems Handout #6 Homework # Solutions. Operations on signals (Lathi& Ding.3-3). For the signal g(t) shown below, sketch: a. g(t 4); b. g(t/.5); c. g(t
More informationInternational Journal of Computer Sciences and Engineering. Research Paper Volume-4, Issue-4 E-ISSN: 2347-2693
International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-4, Issue-4 E-ISSN: 2347-2693 PAPR Reduction Method for the Localized and Distributed DFTS-OFDM System Using
More informationVoice services over Adaptive Multi-user Orthogonal Sub channels An Insight
TEC Voice services over Adaptive Multi-user Orthogonal Sub channels An Insight HP 4/15/2013 A powerful software upgrade leverages quaternary modulation and MIMO techniques to improve network efficiency
More informationEvolution from Voiceband to Broadband Internet Access
Evolution from Voiceband to Broadband Internet Access Murtaza Ali DSPS R&D Center Texas Instruments Abstract With the growth of Internet, demand for high bit rate Internet access is growing. Even though
More informationA Practical Scheme for Wireless Network Operation
A Practical Scheme for Wireless Network Operation Radhika Gowaikar, Amir F. Dana, Babak Hassibi, Michelle Effros June 21, 2004 Abstract In many problems in wireline networks, it is known that achieving
More informationSignal Detection. Outline. Detection Theory. Example Applications of Detection Theory
Outline Signal Detection M. Sami Fadali Professor of lectrical ngineering University of Nevada, Reno Hypothesis testing. Neyman-Pearson (NP) detector for a known signal in white Gaussian noise (WGN). Matched
More information1. (Ungraded) A noiseless 2-kHz channel is sampled every 5 ms. What is the maximum data rate?
Homework 2 Solution Guidelines CSC 401, Fall, 2011 1. (Ungraded) A noiseless 2-kHz channel is sampled every 5 ms. What is the maximum data rate? 1. In this problem, the channel being sampled gives us the
More informationCourse Curriculum for Master Degree in Electrical Engineering/Wireless Communications
Course Curriculum for Master Degree in Electrical Engineering/Wireless Communications The Master Degree in Electrical Engineering/Wireless Communications, is awarded by the Faculty of Graduate Studies
More informationAppendix D Digital Modulation and GMSK
D1 Appendix D Digital Modulation and GMSK A brief introduction to digital modulation schemes is given, showing the logical development of GMSK from simpler schemes. GMSK is of interest since it is used
More informationCDMA TECHNOLOGY. Brief Working of CDMA
CDMA TECHNOLOGY History of CDMA The Cellular Challenge The world's first cellular networks were introduced in the early 1980s, using analog radio transmission technologies such as AMPS (Advanced Mobile
More informationINTRODUCTION TO COMMUNICATION SYSTEMS AND TRANSMISSION MEDIA
COMM.ENG INTRODUCTION TO COMMUNICATION SYSTEMS AND TRANSMISSION MEDIA 9/6/2014 LECTURES 1 Objectives To give a background on Communication system components and channels (media) A distinction between analogue
More informationDigital Communications
Digital Communications Fourth Edition JOHN G. PROAKIS Department of Electrical and Computer Engineering Northeastern University Boston Burr Ridge, IL Dubuque, IA Madison, Wl New York San Francisco St.
More informationOptimal Transmit Spectra for Communication on Digital Subscriber Lines
Optimal Transmit Spectra for Communication on Digital Subscriber Lines Rohit V. Gaikwad and Richard G. Baraniuk Department of Electrical and Computer Engineering Rice University Houston, Texas, 77005 USA
More informationHow To Understand The Theory Of Time Division Duplexing
Multiple Access Techniques Dr. Francis LAU Dr. Francis CM Lau, Associate Professor, EIE, PolyU Content Introduction Frequency Division Multiple Access Time Division Multiple Access Code Division Multiple
More informationOptimal Design of Sequential Real-Time Communication Systems Aditya Mahajan, Member, IEEE, and Demosthenis Teneketzis, Fellow, IEEE
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 55, NO. 11, NOVEMBER 2009 5317 Optimal Design of Sequential Real-Time Communication Systems Aditya Mahajan, Member, IEEE, Demosthenis Teneketzis, Fellow, IEEE
More informationAdvanced Signal Processing 1 Digital Subscriber Line
Advanced Signal Processing 1 Digital Subscriber Line Biljana Badic e-mail: zoom2@sbox.tu-graz.ac.at 1. I n t r o d u c t i o n As a transmission technology, digital subscriber line was originally developed
More informationBroadband Networks. Prof. Dr. Abhay Karandikar. Electrical Engineering Department. Indian Institute of Technology, Bombay. Lecture - 29.
Broadband Networks Prof. Dr. Abhay Karandikar Electrical Engineering Department Indian Institute of Technology, Bombay Lecture - 29 Voice over IP So, today we will discuss about voice over IP and internet
More informationImplementing Digital Wireless Systems. And an FCC update
Implementing Digital Wireless Systems And an FCC update Spectrum Repacking Here We Go Again: The FCC is reallocating 600 MHz Frequencies for Wireless Mics 30-45 MHz (8-m HF) 174-250 MHz (VHF) 450-960 MHz
More informationmin ǫ = E{e 2 [n]}. (11.2)
C H A P T E R 11 Wiener Filtering INTRODUCTION In this chapter we will consider the use of LTI systems in order to perform minimum mean-square-error (MMSE) estimation of a WSS random process of interest,
More informationIN current film media, the increase in areal density has
IEEE TRANSACTIONS ON MAGNETICS, VOL. 44, NO. 1, JANUARY 2008 193 A New Read Channel Model for Patterned Media Storage Seyhan Karakulak, Paul H. Siegel, Fellow, IEEE, Jack K. Wolf, Life Fellow, IEEE, and
More informationNoise Power and SNR Estimation for OFDM Based Wireless Communication Systems
Noise Power and SNR Estimation for OFDM Based Wireless Communication Systems Hüseyin Arslan Department of Electrical Engineering University of South Florida 422 E. Fowler Avenue Tampa, FL- 3362-535, USA
More informationA WEB BASED TRAINING MODULE FOR TEACHING DIGITAL COMMUNICATIONS
A WEB BASED TRAINING MODULE FOR TEACHING DIGITAL COMMUNICATIONS Ali Kara 1, Cihangir Erdem 1, Mehmet Efe Ozbek 1, Nergiz Cagiltay 2, Elif Aydin 1 (1) Department of Electrical and Electronics Engineering,
More informationFull- or Half-Duplex? A Capacity Analysis with Bounded Radio Resources
Full- or Half-Duplex? A Capacity Analysis with Bounded Radio Resources Vaneet Aggarwal AT&T Labs - Research, Florham Park, NJ 7932. vaneet@research.att.com Melissa Duarte, Ashutosh Sabharwal Rice University,
More informationPrivacy and Security in the Internet of Things: Theory and Practice. Bob Baxley; bob@bastille.io HitB; 28 May 2015
Privacy and Security in the Internet of Things: Theory and Practice Bob Baxley; bob@bastille.io HitB; 28 May 2015 Internet of Things (IoT) THE PROBLEM By 2020 50 BILLION DEVICES NO SECURITY! OSI Stack
More informationLecture 2 Outline. EE 179, Lecture 2, Handout #3. Information representation. Communication system block diagrams. Analog versus digital systems
Lecture 2 Outline EE 179, Lecture 2, Handout #3 Information representation Communication system block diagrams Analog versus digital systems Performance metrics Data rate limits Next lecture: signals and
More informationExample/ an analog signal f ( t) ) is sample by f s = 5000 Hz draw the sampling signal spectrum. Calculate min. sampling frequency.
1 2 3 4 Example/ an analog signal f ( t) = 1+ cos(4000πt ) is sample by f s = 5000 Hz draw the sampling signal spectrum. Calculate min. sampling frequency. Sol/ H(f) -7KHz -5KHz -3KHz -2KHz 0 2KHz 3KHz
More informationHIGH SIGNAL-TO-NOISE RATIO GAIN BY STOCHASTIC RESONANCE IN A DOUBLE WELL
Post-print version of the paper: Zoltan Gingl, Peter Makra, and Robert Vajtai, Fluct. Noise Lett., L8 (2). World Scientific Publishing Company. DOI:.42/S29477548 (http://dx.doi.org/.42/s29477548) HIGH
More informationPropagation Channel Emulator ECP_V3
Navigation simulators Propagation Channel Emulator ECP_V3 1 Product Description The ECP (Propagation Channel Emulator V3) synthesizes the principal phenomena of propagation occurring on RF signal links
More informationA SIMULATION STUDY ON SPACE-TIME EQUALIZATION FOR MOBILE BROADBAND COMMUNICATION IN AN INDUSTRIAL INDOOR ENVIRONMENT
A SIMULATION STUDY ON SPACE-TIME EQUALIZATION FOR MOBILE BROADBAND COMMUNICATION IN AN INDUSTRIAL INDOOR ENVIRONMENT U. Trautwein, G. Sommerkorn, R. S. Thomä FG EMT, Ilmenau University of Technology P.O.B.
More informationHD Radio FM Transmission System Specifications Rev. F August 24, 2011
HD Radio FM Transmission System Specifications Rev. F August 24, 2011 SY_SSS_1026s TRADEMARKS HD Radio and the HD, HD Radio, and Arc logos are proprietary trademarks of ibiquity Digital Corporation. ibiquity,
More informationBit Error Rate Performance Analysis on Modulation Techniques of Wideband Code Division Multiple Access
22 Bit Error Rate Performance Analysis on Modulation Techniques of Wideband Code Division Multiple Access M. A. Masud, M. Samsuzzaman, M. A.Rahman Abstract - In the beginning of 21 st century there has
More informationCapacity of the Multiple Access Channel in Energy Harvesting Wireless Networks
Capacity of the Multiple Access Channel in Energy Harvesting Wireless Networks R.A. Raghuvir, Dinesh Rajan and M.D. Srinath Department of Electrical Engineering Southern Methodist University Dallas, TX
More informationThe Applicability of the Tap-Delay Line Channel Model to Ultra Wideband
The Applicability of the Tap-Delay Line Channel Model to Ultra Wideband Liu Yang Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the
More informationQAM Demodulation. Performance Conclusion. o o o o o. (Nyquist shaping, Clock & Carrier Recovery, AGC, Adaptive Equaliser) o o. Wireless Communications
0 QAM Demodulation o o o o o Application area What is QAM? What are QAM Demodulation Functions? General block diagram of QAM demodulator Explanation of the main function (Nyquist shaping, Clock & Carrier
More informationLEVERAGING FPGA AND CPLD DIGITAL LOGIC TO IMPLEMENT ANALOG TO DIGITAL CONVERTERS
LEVERAGING FPGA AND CPLD DIGITAL LOGIC TO IMPLEMENT ANALOG TO DIGITAL CONVERTERS March 2010 Lattice Semiconductor 5555 Northeast Moore Ct. Hillsboro, Oregon 97124 USA Telephone: (503) 268-8000 www.latticesemi.com
More informationDigital Imaging and Multimedia. Filters. Ahmed Elgammal Dept. of Computer Science Rutgers University
Digital Imaging and Multimedia Filters Ahmed Elgammal Dept. of Computer Science Rutgers University Outlines What are Filters Linear Filters Convolution operation Properties of Linear Filters Application
More informationSpike-Based Sensing and Processing: What are spikes good for? John G. Harris Electrical and Computer Engineering Dept
Spike-Based Sensing and Processing: What are spikes good for? John G. Harris Electrical and Computer Engineering Dept ONR NEURO-SILICON WORKSHOP, AUG 1-2, 2006 Take Home Messages Introduce integrate-and-fire
More informationE190Q Lecture 5 Autonomous Robot Navigation
E190Q Lecture 5 Autonomous Robot Navigation Instructor: Chris Clark Semester: Spring 2014 1 Figures courtesy of Siegwart & Nourbakhsh Control Structures Planning Based Control Prior Knowledge Operator
More information8 MIMO II: capacity and multiplexing
CHAPTER 8 MIMO II: capacity and multiplexing architectures In this chapter, we will look at the capacity of MIMO fading channels and discuss transceiver architectures that extract the promised multiplexing
More informationAnalog and Digital Signals, Time and Frequency Representation of Signals
1 Analog and Digital Signals, Time and Frequency Representation of Signals Required reading: Garcia 3.1, 3.2 CSE 3213, Fall 2010 Instructor: N. Vlajic 2 Data vs. Signal Analog vs. Digital Analog Signals
More informationOptimum Frequency-Domain Partial Response Encoding in OFDM System
1064 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL 51, NO 7, JULY 2003 Optimum Frequency-Domain Partial Response Encoding in OFDM System Hua Zhang and Ye (Geoffrey) Li, Senior Member, IEEE Abstract Time variance
More informationNote monitors controlled by analog signals CRT monitors are controlled by analog voltage. i. e. the level of analog signal delivered through the
DVI Interface The outline: The reasons for digital interface of a monitor the transfer from VGA to DVI. DVI v. analog interface. The principles of LCD control through DVI interface. The link between DVI
More informationChapter 10 Introduction to Time Series Analysis
Chapter 1 Introduction to Time Series Analysis A time series is a collection of observations made sequentially in time. Examples are daily mortality counts, particulate air pollution measurements, and
More informationPublic Switched Telephone System
Public Switched Telephone System Structure of the Telephone System The Local Loop: Modems, ADSL Structure of the Telephone System (a) Fully-interconnected network. (b) Centralized switch. (c) Two-level
More information