Modern Digital and Analog Communication Systems 5th Edition by BP Lathi, Zhi Ding ISBN 0190686847 9780190686840

1 INTRODUCTION

1.1 COMMUNICATION SYSTEMS

1.2 DESIGN CHALLENGES: CHANNEL DISTORTIONS AND NOISES

1.3 MESSAGE SOURCES

1.3.1 The Digital Revolution in Communications

1.3.2 Distortionless Regeneration of Digital Signals

1.3.3 Analog-to-Digital (A/D) Conversion for Digital Communications

1.3.4 Pulse-Coded Modulation—A Digital Representation

1.4 CHANNEL EFFECT, SIGNAL-TO-NOISE RATIO, AND CAPACITY

1.4.1 Signal Bandwidth and Power

1.4.2 Channel Capacity and Data Rate

1.5 MODULATION AND DETECTION

1.5.1 Ease of Emission/Transmission

1.5.2 Simultaneous Transmission of Multiple Signals—Multiplexing

1.5.3 Demodulation

1.6 DIGITAL SOURCE CODING AND ERROR CORRECTION CODING

1.7 A BRIEF HISTORICAL REVIEW OF MODERN TELECOMMUNICATIONS

REFERENCES

2 SIGNALS AND SIGNAL SPACE

2.1 SIZE OF A SIGNAL

2.2 CLASSIFICATION OF SIGNALS

2.2.1 Continuous Time and Discrete Time Signals

2.2.2 Analog and Digital Signals

2.2.3 Periodic and Aperiodic Signals

2.2.4 Energy and Power Signals

2.2.5 Deterministic and Random Signals

2.3 SOME USEFUL SIGNAL OPERATIONS

2.3.1 Time Shifting

2.3.2 Time Scaling

2.3.3 Time Inversion (or Folding)

2.4 UNIT IMPULSE SIGNAL

2.5 SIGNALS VERSUS VECTORS

2.5.1 Component of a Vector along Another Vector

2.5.2 Signal Decomposition and Signal Components

2.5.3 Complex Signal Space and Orthogonality

2.5.4 Energy of the Sum of Orthogonal Signals

2.6 CORRELATION OF SIGNALS

2.6.1 Identical Twins, Opposite Personalities, and Complete Strangers

2.6.2 Application of Signal Correlation in Signal Detection

2.6.3 Correlation Functions

2.6.4 Autocorrelation Function

2.7 ORTHOGONAL SIGNAL SETS

2.7.1 Orthogonal Vector Space

2.7.2 Orthogonal Signal Space

2.7.3 Parseval’s Theorem

2.7.4 Some Examples of Generalized Fourier Series

2.8 TRIGONOMETRIC FOURIER SERIES

2.8.1 Finding Trigonometric Fourier Series for Aperiodic Signals

2.8.2 Existence of the Fourier Series: Dirichlet Conditions

2.8.3 The Fourier Spectrum

2.8.4 The Effect of Symmetry

2.9 FREQUENCY DOMAIN AND EXPONENTIAL FOURIER SERIES

2.9.1 Exponential Fourier Spectra

2.9.2 What Does Negative Frequency Mean?

2.9.3 Parseval’s Theorem in the Fourier Series

2.10 MATLAB EXERCISES

2.10.1 Basic Signals and Signal Graphing

2.10.2 Signal Operations

2.10.3 Periodic Signals and Signal Power

2.10.4 Signal Correlation

2.10.5 Numerical Computation of Coefficients Dn

REFERENCES

PROBLEMS

COMPUTER ASSIGNMENT PROBLEMS

3 ANALYSIS AND TRANSMISSION OF SIGNALS

3.1 FOURIER TRANSFORM OF SIGNALS

3.2 TRANSFORMS OF SOME USEFUL FUNCTIONS

3.3 SOME FOURIER TRANSFORM PROPERTIES

3.3.1 Time-Frequency Duality

3.3.2 Duality Property

3.3.3 Time-Scaling Property

3.3.4 Time-Shifting Property

3.3.5 Frequency-Shifting Property

3.3.6 Convolution Theorem

3.3.7 Time Differentiation and Time Integration

3.4 SIGNAL TRANSMISSION THROUGH A LINEAR TIME-INVARIANT SYSTEM

3.4.1 Signal Distortion during Transmission

3.4.2 Distortionless Transmission

3.5 IDEAL VERSUS PRACTICAL FILTERS

3.6 SIGNAL DISTORTION OVER A COMMUNICATION CHANNEL

3.6.1 Linear Distortion

3.6.2 Distortion Caused by Channel Nonlinearities

3.6.3 Distortion Caused by Multipath Effects

3.6.4 Channels Fading in Time

3.7 SIGNAL ENERGY AND ENERGY SPECTRAL DENSITY

3.7.1 Parseval’s Theorem

3.7.2 Energy Spectral Density (ESD)

3.7.3 Essential Bandwidth of a Signal

3.7.4 Energy of Modulated Signals

3.7.5 Time Autocorrelation Function and Energy Spectral Density

3.8 SIGNAL POWER AND POWER SPECTRAL DENSITY

3.8.1 Power Spectral Density (PSD)

3.8.2 Time Autocorrelation Function of Power Signals

3.8.3 Input PSD versus Output PSD

3.8.4 PSD of Modulated Signals

3.9 NUMERICAL COMPUTATION OF FOURIER TRANSFORM: THE DFT

3.10 MATLAB EXERCISES

3.10.1 Computing Fourier Transforms

3.10.2 Illustration of Time-Shifting Property

3.10.3 Filtering

3.10.4 Autocorrelation Function and PSD

REFERENCES

PROBLEMS

4 ANALOG MODULATIONS AND DEMODULATIONS

4.1 BASEBAND VERSUS CARRIER COMMUNICATIONS

4.2 DOUBLE-SIDEBAND AMPLITUDE MODULATION

4.2.1 Demodulation of DSB-SC Modulation Signals

4.2.2 Amplitude Modulators

4.3 AMPLITUDE MODULATION (AM)

4.3.1 Sideband Power, Carrier Power, and Modulation Efficiency

4.3.2 Demodulation of AM Signals

4.4 BANDWIDTH-EFFICIENT AMPLITUDE MODULATIONS

4.4.1 Amplitude Modulation: Single-sideband (SSB)

4.4.2 Quadrature Amplitude Modulation (QAM)

4.4.3 Amplitude Modulations: Vestigial Sideband (VSB)

4.4.4 Receiver Carrier Synchronization for Coherent Detection

4.5 FM AND PM: NONLINEAR ANGLE MODULATIONS

4.6 BANDWIDTH ANALYSIS OF ANGLE MODULATIONS

4.7 DEMODULATION OF FM SIGNALS

4.8 FREQUENCY CONVERSION AND SUPERHETERODYNE RECEIVERS

4.9 GENERATING FM SIGNALS

4.10 FREQUENCY DIVISION MULTIPLEXING (FDM)

4.11 PHASE-LOCKED LOOP AND APPLICATIONS

4.11.1 Phase-Locked Loop (PLL)

4.11.2 Case Study: Carrier Acquisition in DSB-SC

4.12 MATLAB EXERCISES

4.12.1 DSB-SC Modulation and Demodulation

4.12.2 AM Modulation and Demodulation

4.12.3 SSB-SC Modulation and Demodulation

4.12.4 QAM Modulation and Demodulation

4.12.5 FM Modulation and Demodulation

REFERENCES

PROBLEMS

COMPUTER ASSIGNMENT PROBLEMS

5 DIGITIZATION OF ANALOG SOURCE SIGNALS

5.1 SAMPLING THEOREM

5.1.1 Uniform Sampling

5.1.2 Signal Reconstruction from Uniform Samples in D/A Conversion

5.1.3 Practical Issues in Sampling and Reconstruction

5.1.4 Maximum Information Rate through Finite Bandwidth

5.1.5 Nonideal Practical A/D Sampling Analysis

5.1.6 Pulse-Modulations by Signal Samples

5.2 PULSE CODE MODULATION (PCM)

5.2.1 Advantages of Digital Communication

5.2.2 Quantizing

5.2.3 Progressive Taxation: Nonuniform Quantization

5.2.4 Transmission Bandwidth and the Output SNR

5.3 DIGITAL TELEPHONY: PCM IN T1 SYSTEMS

A HISTORICAL NOTE

5.4 DIGITAL MULTIPLEXING HIERARCHY

5.4.1 Signal Format

5.4.2 Asynchronous Channels and Bit Stuffing

5.4.3 Plesiochronous (almost Synchronous) Digital Hierarchy

5.5 DIFFERENTIAL PULSE CODE MODULATION (DPCM)

5.5.1 Analysis of DPCM

5.5.2 ADAPTIVE DIFFERENTIAL PCM (ADPCM)

5.6 DELTA MODULATION

5.7 VOCODERS AND VIDEO COMPRESSION

5.7.1 Linear Prediction Coding Vocoders

5.7.2 Video Encoding for Transmission

5.8 MATLAB EXERCISES

5.8.1 Sampling and Reconstruction of Lowpass Signals

5.8.2 PCM Illustration

5.8.3 Delta Modulation

5.8.4 Video Residual Image Compression and Coding

REFERENCES

PROBLEMS

COMPUTER ASSIGNMENT PROBLEMS

6 PRINCIPLES OF DIGITAL DATA TRANSMISSION

6.1 DIGITAL COMMUNICATION SYSTEMS

6.1.1 Source

6.1.2 Line Codes

6.1.3 Multiplexer

6.1.4 Regenerative Repeater

6.2 BASEBAND LINE CODING

6.2.1 PSD of Various Baseband Line Codes

6.2.2 Polar Signaling

6.2.3 Constructing a DC Null in PSD by Pulse Shaping

6.2.4 On-Off Signaling

6.2.5 Bipolar Signaling

6.3 PULSE SHAPING

6.3.1 Intersymbol Interferences (ISI) and Effect

6.3.2 Nyquist’s First Criterion for Zero ISI

6.3.3 Controlled ISI or Partial Response Signaling

6.3.4 Example of a Duobinary Pulse

6.3.5 Pulse Relationship between Zero-ISI, Duobinary, and Modified Duobinary

6.3.6 Detection of Duobinary Signaling and Differential Encoding

6.3.7 Pulse Generation

6.4 SCRAMBLING

6.5 DIGITAL RECEIVERS AND REGENERATIVE REPEATERS

6.5.1 Equalizers

6.5.2 Timing Extraction

6.5.3 Detection Error

6.6 EYE DIAGRAMS: AN IMPORTANT DIAGNOSTIC TOOL

6.7 PAM: M-ARY BASEBAND SIGNALING

6.8 DIGITAL CARRIER SYSTEMS

6.8.1 Basic Binary Carrier Modulations

6.8.2 PSD of Digital Carrier Modulation

6.8.3 Connections between Analog and Digital Carrier Modulations

6.8.4 Demodulation

6.9 M-ARY DIGITAL CARRIER MODULATION

6.10 MATLAB EXERCISES

6.10.1 Baseband Pulseshaping and Eye Diagrams

6.10.2 PSD Estimation

REFERENCES

PROBLEMS

COMPUTER ASSIGNMENT PROBLEMS

7 FUNDAMENTALS OF PROBABILITY THEORY

7.1 CONCEPT OF PROBABILITY

7.1.1 Relative Frequency and Probability

7.1.2 Conditional Probability and Independent Events

7.1.3 Bernoulli Trials

7.1.4 To Divide and Conquer: The Total Probability Theorem

7.1.5 Axiomatic Theory of Probability

7.2 RANDOM VARIABLES

7.3 STATISTICAL AVERAGES (MEANS)

7.4 CORRELATION

7.5 LINEAR MEAN SQUARE ESTIMATION

7.6 SUM OF RANDOM VARIABLES

7.7 CENTRAL LIMIT THEOREM

REFERENCES

PROBLEMS

8 RANDOM PROCESSES AND SPECTRAL ANALYSIS

8.1 FROM RANDOM VARIABLE TO RANDOM PROCESS

8.2 CLASSIFICATION OF RANDOM PROCESSES

8.3 POWER SPECTRAL DENSITY

8.4 MULTIPLE RANDOM PROCESSES

8.5 TRANSMISSION OF RANDOM PROCESSES THROUGH LINEAR SYSTEMS

8.5.1 Application: Optimum Filtering (Wiener-Hopf Filter)

8.5.2 Application: Performance Analysis of Baseband Analog Systems

8.5.3 Application: Optimum Preemphasis-Deemphasis Systems

8.5.4 Application: Pulse Code Modulation

8.6 BANDPASS RANDOM PROCESSES

8.6.1 Analytical Figure of Merit of Analog Modulations

8.6.2 Application: Performance Analysis of Amplitude Modulations

REFERENCES

PROBLEMS

9 PERFORMANCE ANALYSIS OF DIGITAL COMMUNICATION SYSTEMS

9.1 OPTIMUM LINEAR DETECTOR FOR BINARY POLAR SIGNALING

9.1.1 Binary Threshold Detection

9.1.2 Optimum Receiver Filter—Matched Filter

9.2 GENERAL BINARY SIGNALING

9.2.1 Optimum Linear Receiver Analysis

9.2.2 Performance of General Binary Systems under White Gaussian Noise

9.3 COHERENT RECEIVERS FOR DIGITAL CARRIER MODULATIONS

9.4 SIGNAL SPACE ANALYSIS OF OPTIMUM DETECTION

9.4.1 Geometrical Signal Space

9.4.2 Signal Space and Basis Signals

9.5 VECTOR DECOMPOSITION OF WHITE NOISE RANDOM PROCESSES

9.5.1 Determining Basis Functions for a Random Process

9.5.2 Geometrical Representation of White Noise Processes

9.5.3 White Gaussian Noise

9.5.4 Properties of Gaussian Random Processes

9.6 OPTIMUM RECEIVER FOR WHITE GAUSSIAN NOISE CHANNELS

9.6.1 Geometric Representations

9.6.2 Dimensionality of the Detection Signal Space

9.6.3 MAP: Optimum Receiver for Minimizing Probability of Error

9.6.4 Decision Regions and Error Probability

9.6.5 Multiamplitude Signaling (PAM)

9.6.6 M-ary QAM Analysis

9.7 GENERAL ERROR PROBABILITY OF OPTIMUM RECEIVERS

9.7.1 Multitone Signaling (MFSK)

9.7.2 Bandwidth and Power Trade-offs of M-ary Orthogonal Signals

9.8 EQUIVALENT SIGNAL SETS

9.8.1 Minimum Energy Signal Set

9.8.2 Simplex Signal Set

9.9 NONWHITE (COLORED) CHANNEL NOISE

9.10 OTHER USEFUL PERFORMANCE CRITERIA

9.11 NONCOHERENT DETECTION

9.12 MATLAB EXERCISES

9.12.1 Computer Exercise 9.1: Binary Polar Signaling with Different Pulses

9.12.2 Computer Exercise 9.2: On-Off Binary Signaling

9.12.3 Computer Exercise 9.3: 16-QAM Modulation

9.12.4 Computer Exercise 9.4: Noncoherent FSK Detection

9.12.5 Computer Exercise 9.5: Noncoherent Detection of Binary Differential PSK

REFERENCES

PROBLEMS

COMPUTER ASSIGNMENT PROBLEMS

10 SPREAD SPECTRUM COMMUNICATIONS

10.1 FREQUENCY HOPPING SPREAD SPECTRUM (FHSS) SYSTEMS

10.2 MULTIPLE FHSS USER SYSTEMS AND PERFORMANCE

10.3 APPLICATIONS OF FHSS

10.4 DIRECT SEQUENCE SPREAD SPECTRUM

10.5 RESILIENT FEATURES OF DSSS

10.6 CODE DIVISION MULTIPLE-ACCESS (CDMA) OF DSSS

10.7 MULTIUSER DETECTION (MUD)

10.8 MODERN PRACTICAL DSSS CDMA SYSTEMS

10.8.1 CDMA in Cellular Phone Networks

10.8.2 CDMA in the Global Positioning System (GPS)

10.8.3 IEEE 802.11b Wireless LAN (Wi-Fi) Protocol

10.9 MATLAB EXERCISES

10.9.1 Computer Exercise 10.1: FHSS FSK Communication under Partial Band Jamming

10.9.2 Computer Exercise 10.2: DSSS Transmission of QPSK

10.9.3 Computer Exercise 10.3: Multiuser DS-CDMA System

10.9.4 Computer Exercise 10.4: Multiuser CDMA Detection in Near-Far Environment

REFERENCES

PROBLEMS

COMPUTER ASSIGNMENT PROBLEMS

11 DIGITAL COMMUNICATIONS OVER LINEARLY DISTORTIVE CHANNELS

11.1 LINEAR DISTORTIONS OF WIRELESS MULTIPATH CHANNELS

11.2 RECEIVER CHANNEL EQUALIZATION

11.2.1 Antialiasing Filter versus Matched Filter

11.2.2 Maximum Likelihood Sequence Estimation (MLSE)

11.3 LINEAR T-SPACED EQUALIZATION (TSE)

11.3.1 Zero-Forcing TSE

11.3.2 TSE Design Based on MMSE

11.4 LINEAR FRACTIONALLY SPACED EQUALIZERS (FSE)

11.4.1 The Single-Input–Multiple-Output (SIMO) Model

11.4.2 FSE Designs

11.5 CHANNEL ESTIMATION

11.6 DECISION FEEDBACK EQUALIZER

11.7 OFDM (MULTICARRIER) COMMUNICATIONS

11.7.1 Principles of OFDM

11.7.2 OFDM Channel Noise

11.7.3 Zero-Padded OFDM

11.7.4 Cyclic Prefix Redundancy in OFDM

11.7.5 OFDM Equalization

11.8 DISCRETE MULTITONE (DMT) MODULATIONS

11.9 REAL-LIFE APPLICATIONS OF OFDM AND DMT

11.10 BLIND EQUALIZATION AND IDENTIFICATION

11.11 TIME-VARYING CHANNEL DISTORTIONS DUE TO MOBILITY

11.12 MATLAB EXERCISES

11.12.1 Computer Exercise 11.1: 16-QAM Linear Equalization

11.12.2 Computer Exercise 11.2: Decision Feedback Equalization

11.12.3 Computer Exercise 11.3: OFDM Transmission of QAM Signals

REFERENCES

PROBLEMS

COMPUTER ASSIGNMENT PROBLEMS

12 INTRODUCTION TO INFORMATION THEORY

12.1 MEASURE OF INFORMATION

12.2 SOURCE ENCODING

12.3 ERROR-FREE COMMUNICATION OVER A NOISY CHANNEL

12.4 CHANNEL CAPACITY OF A DISCRETE MEMORYLESS CHANNEL

12.4.1 Definition of Capacity

12.4.2 Error-Free Communication over a BSC

12.5 CHANNEL CAPACITY OF A CONTINUOUS MEMORYLESS CHANNEL

12.5.1 Maximum Entropy for a Given Mean Square Value of x

12.5.2 Mutual Information and Channel Capacity

12.5.3 Error-Free Communication over a Continuous Channel

12.6 FREQUENCY-SELECTIVE CHANNEL CAPACITY

12.7 MULTIPLE-INPUT–MULTIPLE-OUTPUT COMMUNICATION SYSTEMS

12.7.1 Capacity of MIMO Channels

12.7.2 Transmitter without Channel Knowledge

12.7.3 Transmitter with Channel Knowledge

12.8 MATLAB EXERCISES

12.8.1 Computer Exercise 12.1: Huffman Code

12.8.2 Computer Exercise 12.2: Channel Capacity and Mutual Information

12.8.3 Computer Exercise 12.3: MIMO Channel Capacity

REFERENCES

PROBLEMS

COMPUTER ASSIGNMENT PROBLEMS

13 ERROR CORRECTING CODES

13.1 OVERVIEW

13.2 REDUNDANCY FOR ERROR CORRECTION

13.3 LINEAR BLOCK CODES

13.4 CYCLIC CODES

13.5 THE BENEFIT OF ERROR CORRECTION

13.6 CONVOLUTIONAL CODES

13.6.1 Convolutional Encoder

13.6.2 Decoding Convolutional Codes

13.7 TRELLIS DIAGRAM OF BLOCK CODES

13.8 CODE COMBINING AND INTERLEAVING

13.9 SOFT DECODING

13.10 SOFT-OUTPUT VITERBI ALGORITHM (SOVA)

13.11 TURBO CODES

13.12 LOW-DENSITY PARITY CHECK (LDPC) CODES

13.13 MATLAB EXERCISES

13.13.1 Computer Exercise 13.1: Block Decoding

13.13.2 Computer Exercise 13.2: Error Correction in AWGN Channels

REFERENCES

PROBLEMS

COMPUTER ASSIGNMENT PROBLEMS

APPENDIX A ORTHOGONALITY OF SOME SIGNAL SETS

A.1 Trigonometric Sinusoid Signal Set

A.2 Orthogonality of the Exponential Sinusoid Signal Set

APPENDIX B CAUCHY-SCHWARZ INEQUALITY

APPENDIX C GRAM-SCHMIDT ORTHOGONALIZATION OF A VECTOR SET

APPENDIX D BASIC MATRIX PROPERTIES AND OPERATIONS

D.1 Notations

D.2 Matrix Product and Properties

D.3 Identity and Diagonal Matrices

D.4 Determinant of Square Matrices

D.5 Trace

D.6 Eigendecomposition

D.7 Special Hermitian Square Matrices

APPENDIX E MISCELLANEOUS

E.1 L’Hôpital’s Rule

E.2 Taylor and Maclaurin Series

E.3 Power Series

E.4 Sums

E.5 Complex Numbers

E.6 Trigonometric Identities

E.7 Indefinite Integrals

INDEX