Digital Communication - Principles and System Modelling

Preface

Salient Features

Acknowledgements

Contents

Chapter1 Preview and Introduction

1.1 Process of Communication

1.2 General Definition of Signal

1.3 Time-Value Definition of SignalsAnalog and Digital

1.3.1 Continuous Time Continuous Valued Signal

1.3.2 Discrete Time Continuous Valued Signal

1.3.3 Discrete Time Discrete Valued Signal

1.4 Analog and Digital Communication Systems

1.5 Elements of Digital Communication System

1.6 MATLAB Programs

1.6.1 Time and Frequency Domain Representation of Signals

1.6.2 CTSV, DTCV, DTDV Signals

References

Chapter2 Waveform Encoding

2.1 Introduction

2.2 Pulse Code Modulation (PCM)

2.2.1 Process of Sampling

2.2.1.1 Sampling Theorem

Case I

Case II

Case III

2.2.1.2 Aliasing

2.2.2 Process of Quantization

2.2.3 PCM Transmitter and Receiver

2.2.3.1 PCM Transmitter

2.2.3.2 PCM Receiver

2.2.4 Quantization Error

2.2.5 Signal to Noise Ratio (SNR) for Quantized Pulses

2.2.6 Non-uniform Quantization: Companding

2.2.6.1 -Law

2.2.6.2 A-Law

2.3 Differential Pulse Code Modulation (DPCM)

2.3.1 Cumulative Error in PCM

2.3.2 Prevention of Cumulative Error by Applying Feedback

2.3.3 How We Can Predict the Future?

2.3.4 Analysis of DPCM

2.4 Delta Modulation

2.4.1 Drawbacks of Delta Modulation

2.4.1.1 Slope Overloading

2.4.1.2 Granular Noise

2.5 Adaptive Delta Modulation

2.5.1 Song Algorithm

2.5.2 Space-Shuttle Algorithm

2.6 Sigma-Delta Modulation (SDM)

2.6.1 Noise Performance

2.7 Linear Predictive Coder (LPC)

2.7.1 Concept

2.7.2 Genetic Algorithm Based Approach

2.8 MATLAB Programs

2.8.1 Aliasing

References

Chapter3 Digital Baseband Signal Receivers

3.1 Introduction

3.2 Integrate and Dump Type Filter

3.2.1 Noise Power and Variance

3.2.2 Figure of Merit

3.2.3 Probability of Error

3.3 The Optimum Filter

3.4 The Matched Filter

3.4.1 Impulse Response

3.4.2 Probability of Error

3.4.3 Properties of Matched Filter

3.5 The Correlator

3.6 Simulink Communication Block Set Example

Integrate and Dump

Library

Description

Dialog Box

Examples

References

Chapter4 Digital Baseband Signal Transmitter

4.1 Introduction

4.2 Elements of Digital Baseband Communication System

4.2.1 Formatting

4.2.2 Regenerative Repeater

4.3 Properties and Choice of Digital Formats

4.4 Line Coding

4.5 Power Spectrum Density of Different Digital Formats

4.5.1 Unipolar-NRZ

4.5.2 Unipolar-RZ

4.5.3 Polar-NRZ

4.5.4 Polar-RZ

4.5.5 Bipolar-NRZ

4.5.6 Split-Phase (Manchester)

References

Chapter5 Equalization

5.1 Inter-Symbol Interference (ISI)

5.2 Nyquist Criterion for Distortion Less Transmission (Zero ISI)

5.2.1 Criteria in Frequency Domain

5.2.2 Concept of Ideal Nyquist Channel

5.2.3 Limitations of Ideal Solution: Raised Cosine Spectrum

5.3 Eye Pattern

5.3.1 Information Obtained from Eye Pattern

5.4 System Design for Known Channel

5.5 Linear Equalizer

5.5.1 Linear Transversal Filter

5.6 Adaptive Equalizer

References

Chapter6 Digital Modulation Techniques

6.1 Introduction

6.2 Amplitude Shift Keying (ASK)

6.2.1 Mathematical Model

6.2.1.1 On-0Off Keying ( OOK)

6.2.2 ASK Modulator

6.2.3 Binary ASK Demodulator

6.3 Frequency Shift Keying (FSK)

6.3.1 Mathematical Model

6.3.2 BFSK Modulator

6.3.3 FSK Demodulator

6.4 Binary Phase Shift Keying (BPSK)

6.4.1 Mathematical Model

6.4.2 BPSK Modulator

6.4.3 BPSK Demodulator

6.5 Differential Phase Shift Keying (DPSK)

6.5.1 DPSK Modulator

6.5.2 DPSK Demodulator

6.6 Quadrature Phase Shift Keying (QPSK)

6.6.1 Mathematical Model

6.6.2 QPSK Modulator

6.6.3 QPSK Demodulator

6.6.4 Offset QPSK (OQPSK)

6.7 Minimum Shift Keying (MSK)

6.8 Probability of Error for Different Modulation Schemes

6.8.1 Probability of Error in ASK

6.8.2 Probability of Error in FSK

6.8.3 Probability of Error in PSK

6.9 MATLAB Programs

6.9.1 QPSK Waveform

6.9.2 MSK Waveform

References

Chapter7 Spread Spectrum Modulation

7.1 Introduction

7.2 Processing Gain

7.3 Pseudo-Noise (PN) Sequence

7.3.1 Concept: A Hypothetical Experiment

7.3.2 Generation of PN Sequence

7.3.3 Properties of PN Sequence

7.4 Direct Sequence Spread Spectrum (DSSS)

7.4.1 Concept

7.4.2 DSSS with Coherent BPSK

7.4.3 Probability of Error Calculation

7.5 Frequency-Hopped Spread Spectrum

7.5.1 Concept

7.5.2 FHSS with FSK

7.5.3 Rate of Hopping: Fast and Slow

7.6 Application of Spread Spectrum

7.6.1 GPS (Global Positioning System)

7.7 CDMA (Code Division Multiple Access)

7.7.1 Orthogonal Chip Sequence

7.7.2 Gold Sequence

7.7.3 Principle of Operation

7.7.3.1 MUX

7.7.3.2 DMUX

References

Chapter8 Information Theory

8.1 Introduction

8.2 Entropy

8.3 Rate of Information

8.4 Information Sources

8.5 Discrete Memoryless Channel (DMC)

8.5.1 Channel Representation

8.5.2 The Channel Matrix

8.6 Special Channels

8.6.1 Lossless Channel

8.6.2 Deterministic Channel

8.6.3 Noise-Less Channel

8.6.4 Binary Symmetric Channel (BSC)

8.6.4.1 Saturated or Stable Probability of Error for Cascaded BSC Channel

8.6.4.2 Probability Model of Erroneous Detection in Cascaded BSC

8.7 Mutual Information

8.8 Channel Capacity

8.8.1 Gaussian Channel: Shanon-Hartley Theorem

8.9 Entropy Coding

8.9.1 Shanon-Fano Coding

8.9.2 Huffman Coding

8.10 MATLAB Code

8.10.1 Convergence of Pe in Cascaded BSC

References

Chapter9 Error Control Coding

9.1 Introduction

9.2 Scope of Coding

Forward Error Correction

9.3 Linear Block Code

9.3.1 Coding Technique Using Generator Matrix

9.3.2 Syndrome Decoding

9.4 Convolutional Code

9.4.1 Encoder

9.4.1.1 Operation

9.4.1.2 Code Rate

9.4.2 State Diagram

9.4.3 Code Tree

9.4.4 Trellis Diagram

9.4.5 Decoding of Convolutional Code by Viterbi

9.4.5.1 Metric

9.4.5.2 Surviving Path

9.4.5.3 Principle of Decoding

9.5 Cyclic Code

9.5.1 Concept and Properties

9.5.2 Encoder and Decoder

9.5.3 Meggitt Decoder

9.6 BCH Code

9.6.1 Simplified BCH Codes

9.6.2 General BCH Codes

9.6.3 Properties

References

AppendixAElementary Probability Theory

A.1 Concept of Probability

A.1.1 Random Experiments and Sample Space

A.1.2 Events

A.1.3 Probability-Understanding Approaches

A.2 Random Variable

A.3 Mean, Variance, Skew-ness and Kurtosis

A.4 Cumulative Distribution Function (CDF)

A.5 Probability Density Function (PDF)

A.5.1 Uniform PDF

A.5.2 Frequently Used Probability Distribution

A.5.2.1. Bernoulli Distribution

A.5.2.2 Gaussian Distribution

A.5.2.3. Poisson Distribution

A.5.2.4 Rayleigh Distribution

References

Appendix BConvolution and Correlation – Some CaseStudies

B.1 Convolution

B.1.1 Basic Properties of Convolution

B.1.1.1 Commutative Law

B.1.1.2 Associative Law

B.1.1.3 Distributive Law

B.1.1.4 Transformed Domain Simplicity

B.1.2 Case 1: Periodicity of Sampled Spectra

B.1.3 Case 2: Transmission of Normally Distributed Information

B.1.4 Case 3: Long Multiplication Using Convolution

B.2 Correlation

B.2.1 Case Study: Pattern (Shape Feature) Matching Between Two Objects Using Cross-Correlation

References

AppendixC Frequently Used MATLAB Functions

plot()

Description

imshow()

Description

drawnow()

Description

Remarks

Examples

stairs()

int2str()

Description

Examples

conv()

ginput()

Interactive Plotting

spline()

Description

Exceptions

Example

Reference

Index