دانلود کتاب Discrete Communication Systems

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Cover
Discrete Communication Systems
Copyright
Dedication
Preface
Acknowlegements
Contents
List of Symbols, Functions, Operators, and Abbreviations
Symbols
Greek and Cyrillic Symbols
Defined Functions
Operators
Abbreviations
1: Introduction to Communication Systems
1.1 Communication Systems and Networks
1.2 Classification of Signals and Systems
1.2.1 Classification of Signals with Respect to Time and Value
1.2.2 Periodic and Symmetric Signals
1.2.3 Deterministic and Stochastic Signals
1.2.4 Classification of Signals with Respect to Power and Energy
1.2.5 Classification of Signals with Respect to Realizability
1.2.6 Classification of Systems
1.3 Conversions of Analogue and Digital Signals
1.3.1 Analogue-to-Digital Conversion
1.3.2 Digital-to-Analogue Conversion
1.3.3 Application of Signals in Digital and Discrete Communication Systems
2: Orthogonal Signals and the Orthogonalization Procedure
2.1 Introduction
2.2 Geometric Representation of Signals
2.2.1 Orthonormal Basis Functions
2.2.2 Vector Representation of Signals
2.3 The Gram–Schmidt Orthogonalization Procedure
2.4 Continuous-Time Orthogonal Signals
2.4.1 Continuous-Time Versus Discrete-Time Basis Signals
2.4.2 Orthonormal Signals
2.4.3 The Gram–Schmidt Orthogonalization Procedure
2.5 Orthogonal Signals in Code Division Multiple Access Communication Systems
Problems
3: Discrete-Time Stochastic Processes
3.1 Definition and Analysis of Discrete-Time Stochastic Processes
3.1.1 Introduction
3.1.2 Definition of a Stochastic Process
3.1.3 Mathematical Analysis of Stochastic Processes
3.2 Statistical Properties of Stochastic Processes
3.2.1 First-Order Statistics
3.2.2 Second-Order Statistics
3.2.3 Higher-Order Statistics
3.2.4 Types of Discrete-Time Stochastic Processes
3.3 The Stationarity of Discrete-Time Stochastic Processes
3.3.1 The Stationarity of One Discrete-Time Stochastic Process
3.3.2 Properties of the Autocorrelation Function
3.3.3 The Stationarity of Two Discrete-Time Stochastic Processes
3.4 Ergodic Processes
3.4.1 Ensemble Averages and Time Averages
3.4.2 Ergodic Processes
3.4.3 Estimate of the Mean across the Ensemble of Realizations of X(n)
3.4.4 Estimate of the Mean across a realization of X(n)
3.4.5 Estimate of the Mean of an Ergodic Process X(n)
3.4.6 Summary of Ergodic Stochastic Processes
3.5 The Frequency-Domain Representation of Discrete-Time Stochastic Processes
3.5.1 Continuous-Time Stochastic Processes in the Frequency Domain
3.5.2 Discrete-Time Stochastic Processes in the Frequency Domain
3.5.3 Cross-Spectrum Functions
3.6 Typical Stochastic Processes
3.6.1 Noise Processes
3.6.2 General Gaussian Noise Processes
3.6.3 Harmonic Processes
3.6.4 Stochastic Binary Processes
3.7 Linear Systems with Stationary Random Inputs
3.7.1 An LTI System with Stationary Random Inputs in the Time Domain
3.7.2 Frequency-Domain Analysis of an LTI System
3.8 Summary
Problems
4 Noise Processes in Discrete Communication Systems
4.1 Gaussian Noise Processes in the Continuous-Time Domain
4.1.1 Continuous White Gaussian Noise Processes
4.1.2 The Entropy of White Gaussian Noise Processes
4.1.3 Truncated Gaussian Noise Processes
4.1.4 Concluding Notes on Gaussian Noise Processes
4.2 Gaussian Noise Processes in the Discrete-Time Domain
4.2.1 White Gaussian Noise Processes with Discrete-Time and Continuous-Valued Samples
4.2.2 Discrete-Time White Gaussian Noise Processes with Discrete-Valued Samples
4.2.3 White Gaussian Noise Processes with Quantized Samples in a Strictly Limited Interval
4.2.4 Band-Limited Continuous- and Discrete-Time Signals and Noise
4.3 Operation of a Baseband Noise Generator
4.3.1 Band-Limited Continuous-Time Noise Generators
4.3.2 Band-Limited Discrete-Time Noise Generators
4.3.3 Spectral Analysis of Continuous-Time Baseband Noise
4.3.4 Spectral Analysis of Discrete-Time Baseband Noise
4.4 Operation of a Bandpass Noise Generator
4.4.1 Ideal Bandpass Continuous-Time Gaussian Noise
4.4.2 Ideal Bandpass Discrete Gaussian Noise
4.4.3 Modulators and Demodulators of Ideal Bandpass Discrete Gaussian Noise
4.5 Practical Design of a Band-Limited Discrete-Time Noise Modulator
4.6 Design of an Ordinary Band-Limited Discrete-Time Noise Modulator
Problems
5: Operation of a Discrete Communication System
5.1 Structure of a Discrete System
5.2 Operation of a Discrete Message Source
5.3 Operation of a Discrete Modulator
5.4 Additive White Gaussian Noise Channels in a Discrete-Time Domain
5.5 Correlation Demodulators
5.5.1 Operation of a Correlator
5.5.2 Statistical Characterization of Correlator Output
5.5.3 Signal Constellation
5.6 Optimum Detectors
5.6.1 The Maximum Likelihood Estimator of a Transmitted Signal
5.6.2 Application of the Maximum Likelihood Rule
5.6.3 Design of an Optimum Detector
5.6.4 Generic Structure of a Discrete Communication System
5.7 Multilevel Systems with a Binary Source
5.7.1 Transmitter Operation
5.7.2 Radio Frequency Blocks and Additive White Gaussian Noise Waveform Channels
5.7.3 Operation of a Bandpass Noise Generator
5.7.4 Intermediate-Frequency Optimum Receivers
5.7.5 Intermediate-Frequency Optimum Detectors
5.8 Operation of a Digital Communication System
5.8.1 Digital versus Discrete Communication Systems
5.8.2 Generic Structure of a Digital Communication System
Appendix: Operation of a Correlator in the Presence of Discrete White
Gaussian Noise
6: Digital Bandpass Modulation Methods
6.1 Introduction
6.2 Coherent Binary Phase-Shift Keying Systems
6.2.1 Operation of a Binary Phase-Shift Keying System
6.2.2 Transmitter Operation
6.2.2.1 Modulating Signal Presentation
6.2.2.2 Modulated Signals in Time and Frequency Domains
6.2.3 Receiver Operation
6.2.3.1 Correlation Demodulator Operation
6.2.3.2 Operation of the Optimum Detector, and Structure of the Receiver
6.2.3.3 Bit Error Probability Calculation
6.3 Quadriphase-Shift Keying
6.3.1 Operation of a Quadrature Phase-Shift Keying System
6.3.2 Transmitter Operation
6.3.2.1 Modulating Signals in Time and Frequency Domains
6.3.2.2 Modulated Signals in the Time Domain
6.3.2.3 Modulated Signals in the Frequency Domain
6.3.2.4 The Power Spectral Density of Signals in a Quadriphase-Shift Keying System
6.3.3 Receiver Operation
6.3.3.1 Operation of the Correlation Demodulator and the Optimum Detector
6.3.3.2 Bit Error Probability Calculation
6.3.3.3 Signal Analysis and Transceiver Structure in a Quadrature Phase-Shift Keying System
6.4 Coherent Binary Frequency-Shift Keying with a Continuous Phase
6.4.1 Operation of a Binary Frequency-Shift Keying System
6.4.2 Transmitter Operation
6.4.2.1 Modulating Signals in Time and Frequency Domains
6.4.2.2 Modulated Signals in the Time Domain and the Signal-Space Diagram
6.4.2.3 Modulating and Modulated Signals in Time and Frequency Domains
6.4.2.4 Modulated Signals in the Frequency Domain
6.4.3 Receiver Operation
6.4.3.1 Operation of a Correlation Demodulator
6.4.3.2 Operation of an Optimum Detector
6.4.3.3 Calculation of the Bit Error Probability
6.4.3.4 Design of a Transceiver for a Binary Frequency-Shift Keying Signal
6.5 M-ary Quadrature Amplitude Modulation
6.5.1 System Operation
6.5.2 Transmitter Operation
6.5.3 Receiver Operation
Appendix A: Densities of the Correlation Variables X1 and X2 in a Quadrature
Phase-Shift Keying System
Appendix B: Derivatives of Density Functions for a Binary Frequency-Shift
Keying System
Appendix C: Precise Derivation of the Bit Error Probability for a Binary
Frequency-Shift Keying System
Appendix D: Power Spectral Density of a Quadrature Component in a
Frequency-Shift Keying Signal
Problems
7: Discrete Bandpass Modulation Methods
7.1 Introduction
7.2 Coherent Binary Phase-Shift Keying Systems
7.2.1 Operation of a Binary Phase-Shift Keying System
7.2.2 Transmitter Operation
7.2.2.1 Presentation of a Modulating Signal
7.2.2.2 Modulated Signals in Time and Frequency Domains
7.2.2.3 The Power Spectral Density of Binary Phase-Shift Keying Modulated Signals
7.2.3 Receiver Operation
7.2.3.1 Operation of a Correlation Demodulator
7.2.3.2 Operation of an Optimum Detector, and Structure of a Receiver
7.2.3.3 Calculation of the Bit Error Probability
7.3 Quadriphase-Shift Keying
7.3.1 System Operation
7.3.2 Transmitter Operation
7.3.2.1 Modulating Signals in Time and Frequency Domains
7.3.2.2 Modulated Signals in the Time Domain
7.3.2.3 Modulated Signals in the Frequency Domain
7.3.3 Receiver Operation
7.3.3.1 Operation of the Correlation Demodulator and the Optimum Detector
7.3.3.2 Calculation of the Bit Error Probability
7.3.3.3 Signal Analysis and Structure of the Transceiver in a Quadriphase-Shift Keying System
7.4 Coherent Binary Frequency-Shift Keying with Continuous Phase
7.4.1 Operation of a Binary Frequency-Shift Keying System
7.4.2 Transmitter Operation
7.4.2.1 Modulating Signals in Time and Frequency Domains
7.4.2.2 Modulated Signal Analysis in the Time Domain and a Signal-Space Diagram
7.4.2.3 Modulated Signal Analysis in Time and Frequency Domains
7.4.2.4 Modulated Signals in the Frequency Domain
7.4.3 Receiver Operation
7.4.3.1 Operation of the Correlation Demodulator
7.4.3.2 Operation of the Optimum Detector
7.4.3.3 Calculation of the Bit Error Probability
7.4.3.4 Transceiver Design for a Binary Frequency-Shift Keying Signal
7.5 M-ary Discrete Quadrature Amplitude Modulation
7.5.1 Operation of a Discrete M-ary Quadrature Amplitude Modulation System
7.5.2 Transmitter Operation
7.5.3 Operation of a Correlation Demodulator
7.5.4 Operation of an Optimum Detector
Appendix A: Power Spectral Density of a Quadriphase-Shift Keying Modulating Signal
Appendix B: Probability Density Functions for a Quadriphase-Shift Keying System
Appendix C: Density Functions for X1 and X2 in a Frequency-Shift Keying System
Appendix D: Statistics of the Decision Variable X = X1 – X2
Problems
8: Orthogonal Frequency Division Multiplexing and Code Division Multiple Access Systems
8.1 Introduction
8.2 Digital Orthogonal Frequency Division Multiplexing Systems
8.2.1 Introduction
8.2.2 Transmitter Operation
8.2.3 Receiver Operation
8.2.4 Operation of a Receiver in the Presence of Noise
8.3 Discrete Orthogonal Frequency Division Multiple Access Systems
8.3.1 Principles of Discrete Signal Processing in an Orthogonal Frequency Division Multiple Access System
8.3.2 A Discrete Baseband Orthogonal Frequency Division Multiple Access System Based on Binary Phase-Shift Keying
8.3.3 Structure and Operation of a Discrete Orthogonal Frequency Division Multiple Access System
8.3.4 Operation of the Receiver in an Orthogonal Frequency Division Multiple Access System
8.3.5 Operation of the Receiver in the Presence of Noise
8.4 Discrete Code Division Multiple Access Systems
8.4.1 Principles of Operation of a Discrete Code Division Multiple
Access System
8.4.2 Derivation of the Probability of Error
Problems
9: Information Theory and Channel Coding
9.1 Characterization of a Discrete Source
9.2 Characterization of a Discrete Channel
9.2.1 A Discrete Memoryless Channel
9.2.2 Discrete Binary Channels with and without Memory
9.2.2.1 Discrete Binary Channels
9.2.2.2 Discrete Binary Memoryless Channels
9.2.2.3 Discrete Binary Channels with Memory
9.2.3 Capacity of a Discrete Memoryless Channel
9.2.3.1 Capacity of a Discrete Channel
9.2.3.2 Example of the Capacity of a Binary Memoryless Channel
9.3 Characterization of Continuous Channels
9.3.1 Differential Entropy
9.3.2 Channel Information for Random Vectors
9.3.3 Definition of the Capacity of a Continuous Channel
9.3.4 Proof of the Channel Capacity Theorem
9.4 Capacity Limits and the Coding Theorem
9.4.1 Capacity Limits
9.4.2 The Coding Theorem and Coding Channel Capacity
9.5 Information and Entropy of Uniform Density Functions
9.5.1 Continuous Uniform Density Functions
9.5.2 Discrete Uniform Density Functions
9.6 Information and Entropy of Gaussian Density Functions
9.6.1 Continuous Gaussian Density Functions
9.6.2 Discrete Gaussian Density Functions
9.7 Block Error Control Codes
9.7.1 Theoretical Basis and Definitions of Block Code Terms
9.7.2 Coding Procedure Using a Generator Matrix
9.7.3 Error Detection Using a Parity Check Matrix
9.7.4 Standard Array Decoding
9.7.5 Syndrome Table Decoding
9.8 Convolutional Codes
9.8.1 Linear Convolutional Codes
9.8.2 Operation of a Coding Communication System
9.8.3 Operation of a Decoder
9.8.4 Decoding Algorithms
9.9 Introduction to Iterative Decoding and Turbo Decoding
9.9.1 Coding Models for Communication Systems
9.9.2 The Hard-Output Viterbi Algorithm
9.9.3 Iterative Algorithms and Turbo Coding
Appendix A: Derivation of Mutual Information
Appendix B: Entropy of a Truncated Discrete Gaussian Density Function Problems
Problems
10: Designing Discrete and Digital Communication Systems
10.1 Introduction
10.2 Designing Quadriphase-Shift Keying Transceivers
10.2.1 Quadriphase-Shift Keying Systems with Digital-to-Analogue and Analogue-to-Digital Conversion of Modulating Signals
10.2.1.1 Quadriphase-Shift Keying Transmitters with Baseband Discrete-Time Signal Processing
10.2.1.2 Designing a Quadriphase-Shift Keying Receiver
10.2.1.3 Practical Design of a Quadriphase-Shift Keying Receiver
10.2.2 Quadriphase-Shift Keying Systems with Digital-to-Analogue and Analogue-to-Digital Conversion of Intermediate-Frequency Signals
10.2.2.1 Designing a Digital Quadriphase-Shift Keying Transmitter at Intermediate Frequency
10.2.2.2 Design of a Digital Quadriphase-Shift Keying Receiver at Intermediate Frequency
10.2.2.3 Practical Design of a Discrete Quadriphase-Shift Keying Receiver at Intermediate Frequency
10.3 Designing Quadrature Amplitude Modulation Transceivers
10.3.1 Quadrature Amplitude Modulation Systems with Digital-to-Analogue and Analogue-to-Digital Conversion of Modulating Signals
10.3.1.1 Quadrature Amplitude Modulation Transmitters with Baseband Discrete-Time Signal Processing
10.3.1.2 Designing a Quadrature Amplitude Modulation Receiver
10.3.1.3 Practical Design of a Quadrature Amplitude Modulation Receiver
10.3.2 Quadrature Amplitude Modulation Systems with Digital-to-Analogue and Analogue-to-Digital Conversion of Intermediate-Frequency Signals
10.3.2.1 Digital Design of a Quadrature Amplitude Modulation Transmitter at Intermediate Frequency
10.3.2.2 Digital Design of a Quadrature Amplitude Modulation Receiver at Intermediate Frequency
10.3.2.3 Practical Design of a Discrete Quadrature Amplitude Modulation Receiver at Intermediate Frequency
10.4 Overview of Discrete Transceiver Design
10.4.1 Introduction
10.4.2 Designing Quadrature Amplitude Modulation Systems
11: Deterministic Continuous-Time Signals and Systems
11.1 Basic Continuous-Time Signals
11.2 Modification of the Independent Variable
11.3 Combined Modifications of the Independent Variable
11.4 Cross-Correlation and Autocorrelation
11.4.1 The Cross-Correlation Function
11.4.2 The Autocorrelation Function
11.5 System Classification
11.6 Continuous-Time Linear-Time-Invariant Systems
11.6.1 Modelling of Systems in the Time Domain
11.6.2 Representation of an Input Signal
11.6.3 Basic Representation of a Linear-Time-Invariant System
11.6.4 Representation of an Output Signal
11.6.5 Properties of Convolution
11.7 Properties of Linear-Time-Invariant Systems
Problems
12: Transforms of Deterministic Continuous-Time Signals
12.1 Introduction
12.2 The Fourier Series
12.2.1 The Fourier Series in Trigonometric Form
12.2.2 An Example of Periodic Signal Analysis
12.2.3 The Fourier Series in Complex Exponential Form
12.2.4 Amplitude Spectra, Magnitude Spectra, and Phase Spectra of Periodic Signals
12.2.5 The Power and Energy of Signals, and Parseval’s Theorem
12.2.6 Existence of Fourier Series
12.2.7 Orthogonality Characteristics of the Fourier Series
12.2.8 Table of the Fourier Series
12.3 Fourier Transform of Continuous Signals
12.3.1 Derivative and Application of Fourier Transform Pairs
12.3.2 Convergence Conditions
12.3.3 The Rayleigh Theorem and the Energy of Signals
12.3.4 Properties of the Fourier Transform
12.3.5 Important Problems and Solutions
12.3.6 Tables of the Fourier Transform
12.4 Fourier Transform of Periodic Signals
12.5 Correlation Functions, Power Spectral Densities, and Linear-Time-Invariant Systems
12.5.1 Correlation of Real-Valued Energy Signals
12.5.2 Correlation of Real-Valued Power Signals
12.5.3 Comprehensive Analysis of Linear-Time-Invariant Systems
12.5.3.1 System Presentation
12.5.3.2 Correlation and Energy Spectral Density of Complex Energy Signals
12.5.3.3 Correlation and Power Spectral Density of Complex Power Signals
12.5.3.4 Analysis of a Linear-Time-Invariant System with Deterministic Energy Signals
12.5.4 Tables of Correlation Functions and Related Spectral Densities
Problems
13: Sampling and Reconstruction of Continuous-Time Signals
13.1 Introduction
13.2 Sampling of Continuous-Time Signals
13.3 Reconstruction of Analogue Signals
13.4 Operation of a Lowpass Reconstruction Filter
13.5 Generation of Discrete-Time Signals
14: Deterministic Discrete-Time Signals and Systems
14.1 Discrete-Time Signals
14.1.1 Elementary Discrete-Time Signals
14.1.2 Modification of Independent Variables
14.1.3 Cross-Correlation and Autocorrelation Functions
14.2 Discrete-Time Systems
14.2.1 Systems Classification
14.2.2 Discrete-Time Linear-Time-Invariant Systems
14.3 Properties of Linear-Time-Invariant Systems
14.4 Analysis of Linear-Time-Invariant Systems in Time and Frequency Domains
Problems
15: Deterministic Discrete-Time Signal Transforms
15.1 Introduction
15.2 The Discrete-Time Fourier Series
15.2.1 Continuous-Time Fourier Series and Transforms
15.2.2 The Discrete-Time Fourier Series
15.2.3 Fourier Series Examples Important for Communication Systems
15.3 The Discrete-Time Fourier Transform
15.3.1 Derivation of the Discrete-Time Fourier Transform Pair
15.3.2 The Problem of Convergence
15.3.3 Properties of the Discrete-Time Fourier Transform
15.3.4 Tables for the Discrete-Time Fourier Transform
15.4 Discrete Fourier Transforms
15.4.1 Fundamentals of Frequency-Domain Sampling
15.4.2 Discrete Fourier Transforms
15.4.3 Inverse Discrete Fourier Transforms
15.4.4 Three Typical Cases of Discrete Fourier Transforms
15.5 Algorithms for Discrete Fourier Transforms
15.5.1 Goertzel’s Algorithm
15.5.2 Discrete Fourier Transforms as Linear Transformations
15.5.3 The Radix-2 Fast Fourier Transform Algorithm
15.6 Correlation and Spectral Densities of Discrete-Time Signals
15.6.1 Cross-Correlation and Correlation of Real-Valued Energy Signals
15.6.2 Cross-Correlation and Correlation of Real-Valued Power Signals
15.6.3 Parseval’s Theorem and the Wiener–Khintchine Theorem
15.6.4 Comprehensive Analysis of Discrete Linear-Time-Invariant Systems
15.6.4.1 System Presentation
15.6.4.2 Correlation and Power Spectral Density of Complex Energy Signals
15.6.4.3 Correlation of Complex Power Signals
15.6.4.4 Analysis of a Linear-Time-Invariant System with Energy Signals
15.6.5 Tables of Correlation Functions and Related Spectral Density Functions
15.7 The z-Transform
15.7.1 Introduction
15.7.2 Derivation of Expressions for the z-Transform
15.7.3 Properties of the z-Transform
15.7.4 The Inverse z-Transform
Problems
16: Theory of the Design, and Operation of Digital Filters
16.1 The Basic Concept of Filtering
16.2 Ideal and Real Transfer Functions
16.3 Representation of Digital Filters
16.4 Basic Finite Impulse Response Filters
16.5 Structures of Finite Impulse Response Filters
16.6 Basic Infinite Impulse Response Filters
16.7 Structures of Infinite Impulse Response Filters
16.7.1 Introduction
16.7.2 Conventional Description of Block Diagrams
16.7.3 Direct Forms of Infinite Impulse Response Filters
16.8 Algorithms for the Design of Digital Filters
16.8.1 Ideal and Real Frequency Responses
16.8.2 Basic Methods for the Design of Digital Filters
16.8.3 Algorithms Based on Iterative Optimization
17: Multi-Rate Discrete-Time Signal Processing
17.1 Multi-Rate Signals in Time and Frequency Domains
17.1.1 Time-Domain Analysis
17.1.2 Frequency-Domain Analysis
17.1.3 Complex Multi-Rate Systems
17.1.4 Complexity Reduction
17.2 Multi-Rate Systems
17.2.1 Basic System Structures
17.2.2 System Analysis in Time and Frequency Domains
17.3 Reduction of Computational Complexity
17.3.1 Multistage Decimators and Interpolators
17.3.2 Polyphase Decomposition of a Decimation Filter
17.3.3 Polyphase Decomposition of a Finite Impulse Response Transfer Function
17.3.4 Polyphase Decomposition of an Infinite Impulse Response Transfer Function
Problems
18: Multi-Rate Filter Banks
18.1 Digital Filter Banks
18.2 Two-Channel Quadrature Mirror Filter Banks
18.2.1 Basic Theory
18.2.2 Elimination of Aliasing in Two-Channel Quadrature Mirror Filter Banks
18.3 Perfect Reconstruction of Two-Channel Filter Banks
18.4 Multichannel Quadrature Mirror Filter Banks
18.5 Multilevel Filter Banks and Adaptive Filter Banks
18.5.1 Banks with Equal or Unequal Passband Widths
18.5.2 Adaptive Filter Banks
Problems
19: Continuous-Time Stochastic Processes
19.1 Continuous-Time Stochastic Processes
19.1.1 Probability, Random Variables, and Stochastic Processes
19.1.2 Statistical Analysis of Stochastic Processes
19.2 Statistical Properties of Stochastic Processes
19.2.1 First- and Second-Order Properties of Stochastic Processes
19.2.2 Types of Stochastic Processes
19.2.3 Entropy of Stochastic Processes and White Noise
19.3 Stationary and Ergodic Stochastic Processes
19.3.1 Stationary Stochastic Processes in Time and Frequency Domains
19.3.1.1 Time Domain Analysis
19.3.1.2 Frequency Domain Analysis
19.3.2 Ergodic Stochastic Processes
19.3.3 Characterization of White Noise Processes
19.3.4 Gaussian Correlated Processes
19.4 Linear-Time Invariant Systems with StationaryStochastic Inputs
19.4.1 Analysis of Linear-Time Invariant Systems in Time and Frequency Domains
19.4.2 Definition of a System Correlation Function for Stochastic Input
19.4.3 Application of the Theory of Linear-Time-Invariant Systems to the Analysis of the Operation of a Lowpass Filter
19.4.4 Analysis of the Operation of a Bandpass Filter
Problems
Bibliography
Index
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