Dene
`Communications
`
`IPR2023-00319
`
`vo
`
`LGE 1030
`LG Electronics, Inc. v. Constellation Designs, LLC
`1
`
`LGE 1030
`LG Electronics, Inc. v. Constellation Designs, LLC
`IPR2023-00319
`
`1
`
`

`

`
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`
`
`CONTENTS
`
`
`Preface
`
`1
`
`Introduction
`1-1 Elements of a Digital Communication System
`1-2. Communication Channels.and Their Characteristics
`1-3 Mathematical Models for Communication Channels
`14 A Historical Perspective in the DevelopmentofDigital
`Communications
`1-5 Overview of the Book
`1-6 Bibliographical Notes and References
`
`2-1-2
`2-1-3
`2-1-4
`2-1-5
`2-1-6
`
`2 Probability and Stochastic Processes
`2-1
`Probability
`.
`2-1-1
`Random Variables, Probability Distributions,
`and Probability Densities
`Functions of Random Variables
`Statistical Averages of Random Variables
`Some Useful Probability Distributions
`Upper bounds on the Tail Probability
`Sums of Random Variables and the Central Limit
`Theorem
`2-2 Stochastic Processes
`2-2-1
`Statistical Averages
`2-2-2
`Power Density Spectrum
`2-2-3
`Response of a Linear Time-Invariant System to a Random
`Input Signal.
`2-2-4
`Sampling Theorem for Band-Limited Stochastic Processes
`2-2-5
`Discrete-Time Stochastic Signals and Systems
`2-2-6
`Cyclostationary Processes
`2-3 Bibliographical Notes and References
`Problems
`
`2
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`3 Source Coding
`3-1 Mathematical Models for Information
`3-2 A Logarithmic Measure of Information
`3-2-1 Average Mutual Information and Entropy
`3-2-2
`Information Measures for Continuous Random Variables
`3-3. Coding for Discrete Sources
`3-3-1 Coding for Discrete Memoryless Sources
`3-3-2 Discrete Stationary Sources
`3-3-3. The Lempel-Ziv Algorithm
`3-4 Coding for Analog Sources—Optimum Quantization
`3-4-1 Rate-Distortion Function
`3-4-2 Scalar Quantization
`3-4-3, Vector Quantization
`3-5 Coding Techniques for Analog Sources
`3-5-1 Temporal Waveform Coding
`3-5-2 Spectral Waveform Coding
`3-5-3 Model-Based Source Coding
`3-6 Bibliographical Notes and References
`Problems
`
`4 Characterization of Communication Signals
`and Systems
`4-1 Representation of Bandpass Signals and Systems
`4-1-1 Representation of Bandpass Signals
`4-1-2. Representation of Linear Bandpass Systems
`4-1-3. Response of a Bandpass System to a Bandpass Signal
`4-1-4 Representation of Bandpass Stationary Stochastic
`Processes
`4-2 Signal Space Representation
`4-2-1 Vector Space Concepts
`4-2-2 Signal Space Concepts
`4-2-3 Orthogonal Expansions of Signals
`4-3 Representation of Digitally Modulated Signals
`4-3-1 Memoryless Modulation Methods
`4-3-2. Linear Modulation with Memory
`4-3-3 Nonlinear Modulation Methods with Memory
`4-4 Spectral Characteristics of Digitally Modulated Signals
`4-4-1 Power Spectsa of Linearly Modulated Signals
`4-4-2 Power Spectra of CPFSK and CPM Signals
`4-4-3 Power Spectra of Modulated Signals with Memory
`4-5 Bibliographical Notes and References
`Problems
`
`5 Optimum Receivers for the Additive White
`Gaussian Noise Channel
`5-1 Optimum Receiver for Signals Corrupted by AWGN
`5-1-1 Correlation Demodulator
`5-1-2 Matched-Filter Demodulator
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`5-2
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`5-3
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`5-4
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`5-1-3. The Optimum Detector
`5-1-4 The Maximum-Likelihood Sequence Detector
`5-1-5 A Symbcl-by-Symbol MAP Detector for Signals
`with Memory
`:
`Performance of the Optimum Receiver for Memoryless
`Modulation
`5-2-1
`Probability of Error for Binary Modulation
`5-2-2 Probability of Error for M-ary Orthogonal Signals
`5-2-3 Probability of Error for M-ary BiorthogonalSignals
`5-2-4 Probability of Error for Simplex Signals
`5-2-5 Probability of Error for M-ary Binary-CodedSignals
`5-2-6 Probability of Error for M-ary PAM
`5-2-1 Probability of Error for M-ary PSK
`5-2-8 Differential PSK (DPSK) and its Performance
`5-2-9 Probability of Error for QAM
`5-2-10 Comparison of Digital Modulation Methods
`Optimum Receiver for CPM Signals
`5-3-1 Optimum Demodulation and Detection of CPM
`5-3-2 Performance of CPM Signals
`5-3-3 Symbol-by-Symbol Detection of CPM Signals
`Optimum Receiver for Signals with Random Phase in AWGN
`Channel
`5-4-1 Optimum Receiver.for Binary Signals
`5-4-2 Optimum Receiver for M-ary Orthogonal Signals
`5-4-3. Probability of Error for Envelope Detection of M-ary
`Orthogonal Signals
`5-4-4 Probability of Error for Envelope Detection of Correlated
`Binary Signals
`Regenerative Repeaters and Link Budget Analysis
`5-5-1 Regenerative Repeaters
`5-5-2 Communication Link Budget Analysis
`5-6
`Bibliographical Notes and References
`Problems
`
`5-5
`
`6-2
`
`6 Carrier and Symbol Synchronization
`6-1
`Signal Parameter Estimation
`6-1-1 The Likelihood Function
`6-1-2. Carrier Recovery and Symbol Synchronization
`in Signal Demodulation
`Carrier Phase Estimation
`6-2-1 Maximum-Likelihood Carrier Phase Estimation
`6-2-2 The Phase-Locked Loop
`6-2-3. Effect of Additive Noise on the Phase Estimate
`6-2-4 Decision-Directed Loops
`6-2-5 Non-Decision-Directed Loops
`Symbol Timing Estimation
`6-3-1 Maximum-Likelihood Timing Estimation
`6-3-2 Non-Decision-Directed Timing Estimation
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`Joint Estimation of Carrier Phase and Symbol Timing
`6-4
`6-5 Performance Characteristics of ML Estimators
`6-6 Bibliographical Notes and References
`Problems
`
`7 Channel Capacity and Coding
`7-1. Channel Models and Channel Capacity
`7-1-1 Channel Models
`7-l-2 Channel Capacity
`7-1-3. Achieving Channel Capacity with Orthogonal Signals
`7-1-4 Channel Reliability Functions
`7-2. Random Selection of Codes
`7-2-1 Random Coding Based on M-ary Binary-Coded Signals
`7-2-2 Random Coding Based on M-ary Multiamplitude Signals
`7-2-3. Comparison of R¥ with the Capacity of the AWGN
`Channel
`7-3 Communication System Design Based on the Cutoff Rate
`7-4 Bibliographical Notes and References
`Problems
`
`8 Block and Convolutional Channel Codes
`8-i Linear Block Codes
`8-1-1 The Generator Matrix and the Parity Check Matrix
`8-1-2.
`Some Specific Linear Block Codes
`8-1-3 Cyclic Codes
`8-1-4 Optimum Soft-Decision Decoding of Linear Block Codes
`8-1-5 Hard-Decision Decoding
`8-1-6 Comparison of Performance between Hard-Decision and
`_
`Soft-Decision Decoding
`8-1-7 Bounds on Minimum Distance of Linear Block Codes
`8-1-8 Nonbinary Block Codes and Concatenated Block Codes
`8-1-9
`Interleaving of Coded Data for Channels with Burst
`Errors
`8-2 Convoiutional Codes
`8-2-1 The Transfer Function of a Convolutional Code
`8-2-2. Optimum Decoding of Convolinional Codes—
`The Viterbi Algorithm
`Probability of Error for Soft-Decision Decoding
`8-2-3.
`Probability of Error far Hard-Decision Decoding
`8-2-4
`8-2-5 Distance Properties of Binary Convolutional Codes
`8-2-6 Nonbinary Dual-k Codes and Concatenated Codes
`8-2-7 Other Decoding Algorithms for Convolutional Codes
`8-2-8
`Practical Considerations in the Application of
`Convolutional Codes
`8-3 Coded Modulation for Bandwidth-Constrained Channels
`8-4 Bibliographical Notes and References
`Problems
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`9
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`10
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`Signal Design for Band-Limited Channels
`9-1 Characterization of Band-Limited Channels
`9-2 Signal Design for Band-Limited Channels
`9-2-1 Design of Band-Limited Signals for No Intersymbol
`Interference—The Nyquist Criterion
`9-2-2 Design of Band-Limited Signals with Controlled ISI—
`Partial-Response Signals
`;
`9-2-3 Data Detection for Controlled ISI
`9-2-4 Signal Design for Channels with Distortion
`9-3 Probability of Error in Detection of PAM
`9-3-1 Probability of Error for Detection of PAM with Zero ISI
`9-3-2 Probability of Error for Detection of Partial-Response
`Signals
`9-3-3. Probability of Error for Optimum Signals in Channel!
`with Distortion
`9-4 Modulation Codes for Spectrum Shaping
`9-5 Bibliographical Notes and References
`Problems
`
`Communication through Band-Limited Linear
`Filter Channels
`10-1 Optimum Receiver for Channels with ISI and AWGN
`10-1-1 Optimum Maximum-Likelihood Receiver
`10-1-2. A Discrete-Time Model for a Channel with ISI
`10-1-3. The Viterbi Algorithm for the Discrete-Time White
`Noise Filter Model
`,
`10-1-4 Performance of MLSE for Channels with ESI
`10-2 Linear Equalization
`10-2-2 Peak Distortion Criterion
`10-2-2_ Mean Square Error (MSE)Criterion
`10-2-3. Performance Characteristics of the MSE Equalizer
`10-2-4 Fractionally Spaced Equalizer
`10-3 Decision-Feedback Equalization
`10-3-1 Coefficient Optimization
`10-3-2. Performance Characteristics of DFE
`10-3-3 Predictive Decision-Feedback Equalizer
`10-4 Bibliographical Notes and References
`Problems
`
`i1
`
`Adaptive Equalization
`11-1 Adaptive Linear Equalizer
`11-1-t The Zero-Forcing Algorithm
`11-1-2. The LMSalgorithm
`11-1-3. Convergence Properties of the LMS Algorithm
`11-14 Excess MSE Due to Noisy Gradient Estimates
`11-1-5 Baseband and Passband Linear Equalizers
`11-2 Adaptive Decision-Feedback Equalizer
`11-2-1 Adaptive Equalization of Trellis-Coded Signals
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`12
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`13
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`li-3) An Adaptive Channel! Estimator for ML Sequence Detection
`11-4 Recursive Least-Squares Algorithms for Adaptive Equalization
`ti-4-1 Recursive Least-Squares (Kalman) Algorithm
`{1-4-2 Linear Prediction and the Lattice Filter
`11-5 Self-Recovering (Blind) Equalization
`{1-5-1 Blind Equalization Based on Maximum-Likelihood
`Criterion
`11-5-2 Stochastic Gradient Algorithms
`}1-5-3 Blind Equalization Algorithms Based on Second-
`and Higher-Order Signal Statistics
`{1-6 Bibliographical Notes and References
`Problems
`
`Multichannel and Multicarrier Systems
`12-1 Muitichanne! Digital Communication in AWGN Channels
`12-1-] Binary Signals
`12-)-2. M-ary Orthogonal Signals
`12-2. Multicarrier Communications
`12-2-] Capacity of a Non-Ideal Linear Filter Channel
`42-2-2) An FFYT-Based Multicarrier System
`12-3 Bibiliographical Notes and References
`Problems
`
`Spread Spectrum Signals for Digital Communications
`13-1 Model of Spread Spectrum Digital Communication System
`13-2. Direct Sequence Spread Spectrum Signals
`13-2-1 Error Rate Performance of the Decoder
`{3-2-2
`Some Applications of DS Spread Spectrum Signals
`{3-2-3 Effect of Pulsed Interference on DS Spread Spectrum
`Systems
`13-2-4 Generation of PN Sequences
`}3-3 Frequency-Hoppped Spread Spectrum Signals
`13-3-1
`Performance of FH Spread Spectrum Signals in AWGN
`Channel
`13-3-2 Performance of FH Spread Spectrum Signals in Partial-
`Band Interference
`13-3-3. A CDMASystem Based on FH Spread Spectrum Signals
`13-4 Other Types of Spread Spectrum Signals
`13-5 Synchronization of Spread Spectrum Signals
`13-6 Bibliographical Notes and References
`Probiems
`
`14
`
`Digital Communication through Fading
`Multipath Channels
`14-1 Characterization of Fading Multipath Channeb
`!4-1-1 Channel Correlation Functions and Power Spectra
`14-]-2
`Statustical Models for Fading Channels
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`14-5
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`14-6
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`14-2
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`14-3
`14-4
`
`The Effect of Characteristics on the Choice
`of a Channel Model
`_
`Frequency-Nonselective, Slowly Fading Channel
`Diversity Techniques for Fading Multipath Channels
`14-4-1 Binary Signals
`14-4-2 Multiphase Signals
`14-4-3 M-ary Orthogonal Signals
`Digital Signaling over a Frequency-Selective, Slowly Fading
`Channel
`14-5-1 A Tapped-Delay-Line Channel Model
`14-5-2. The RAKE Demodulator
`14-5-3 Performance of RAKE Receiver
`Coded Waveforms for Fading Channels
`14-6-1 Probability of Error for Soft-Decision Decoding of Linear
`Binary Block Codes
`14-6-2. Probability of Error for Hard-Decision Decoding of
`Linear Binary Block Codes
`14-6-3 Upper Bounds on the Performance of Convolutional
`Codes for a Raleigh Fading Channel
`14-6-4 Use of Constant-Weight Codes and Concatenated Codes
`for a Fading Channel
`14-6-5 System Design Based on the Cutoff Rate
`14-66 Trellis-Coded Modulation
`14-7
`Bibliographical] Notes and References
`Problems
`
`15 Multiuser Communications
`15-1
`Introduction to Multiple Access Techniques
`15-2
`Capacity of Multiple Access Methods
`15-3
`Code-Division Multiple Access
`15-3-1
`CDMASignal and Channel Models
`15-3-2. The Optimum Receiver
`15-3-3 Suboptimum Detectors
`15-3-4 Performance Characteristics of Detectors
`Random Access Methods
`15-4-1 ALOHA System and Protocols
`1-4-2 Carrier Sense Systems and Protocols
`15-5
`Bibliographical Notes and References
`Problems
`
`15-4
`
`Appendix A The Levinson—Durbin Algorithm
`
`Appendix B= Error Probability for Multichannel
`Binary Signals
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`§ §
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`891
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`893
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`897
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`899
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`917
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`Appendix C Error Probabilities for Adaptive Reception
`of M-phase Signals
`C-1 Mathematical Model for an M-phase Signaling
`Communications System
`C-2 Characteristic Function and Probability Density
`Function of the Phase 8
`C-3 Error Probabilities for Slowly Rayleigh Fading
`Channels
`C-4 Error Probabilities for Time-Invariant and Ricean
`Fading Channels
`
`Appendix D Square-Root Factorization
`References and Bibliography
`Index
`
`9
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`

`

` 1 I
`
`NTRODUCTION
`
`
`In this book, we present the basic principles that underlie the analysis and
`design of digital communication systems. The subject of digital communica-
`tions involves the transmission of information in digital form from a source
`that generates the information to one or more destinations. Of particular
`importance in the analysis and design of communication systems are the
`characteristics of the physical channels through which the information is
`transmitted. The characteristics of the channel generally affect the design of
`the basic building blocks of the communication system. Below, we describe the
`elements of a communication system and their functions.
`
`1-1 ELEMENTS OF A DIGITAL COMMUNICATION
`SYSTEM
`
`Figure 1-1-1 illustrates the functional diagram and the basic elements of a
`digital communication system. The source output may be either an analog
`signal, such as audio or video signal, or a digital signal, such as the output of a
`teletype machine, that is discrete in time and has a finite number of output
`characters. In a digital communication system, the messages produced by the
`source are converted into a sequence of binary digits. Ideally, we should like to
`Tepresent the source output (message) by as few binary digits as possible. In
`other words, we seek an efficient representation of the source output that
`results in little or no redundancy. The process of efficiently converting the
`output of either an analog or digital source into a sequence of binary digits is
`called source encodingor daia compression.
`The sequence of binary digits from the source encoder, which we call the
`
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`
`
`2
`
`DIGITAL COMMUNICATIONS
`
`
`
`Output
`signal
`
`FIGURE1-1-1
`
`Basic elements of a digital communication system.
`
`is passed to the channel encoder. The purpose of the
`information sequence,
`channel encoderis to introduce, in a controlled manner, some redundancy in
`the binary information sequence that can be used at the receiver to overcome
`the effects of noise and interference encountered in the transmission of the
`signal through the channel. Thus, the added redundancy serves to increase the
`reliability of the received data and improves the fidelity of the received signal.
`In effect, redundancy in the information sequenceaids the receiver in decoding
`the desired information sequence. For example, a (trivial) form of encoding of
`the binary information sequence is simply to repeat each binary digit m times,
`where m is some positive integer. More sophisticated (nontrivial) encoding
`involves taking & information bits at a time and mapping each k-bit sequence
`into a unique n-bit sequence, called a code word. The amount of redundancy
`introduced by encoding the data in this manner is measured bythe ratio n/k.
`The reciprocal of this ratio, namely k/x, is called the rate of the code or,
`simply, the code rate.
`The binary sequence at the output of the channel encoder is passed to the
`digital modulator, which serves as the interface to the communications channel.
`Since nearly all of the communication channels encountered in practice are
`capable of transmitting electrical signals (waveforms), the primary purpose of
`the digital modulator is to map the binary information sequence into signal
`waveforms. To elaborate on this point,
`let us suppose that
`the coded
`information sequence is to be transmitted one bit at a time at some uniform
`rate R bits/s. The digital modulator may simply map the binary digit 0 into a
`waveform Sso{f) and the binary digit 1 into a waveform s,(t). In this manner,
`each bit from the channel encoderis transmitted separately. We call this binary
`modulation. Alternatively, the modulator may transmit b coded information
`bits at a time by using Af = 2° distinct waveforms s,(t), i=0,1,...,M~1, one
`waveform for each of the 2? possible b-bit sequences. We call
`this M-ary
`modulation (M>2). Note that a new b-bit sequence enters the modulator
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`
`
`CHAPTER |:
`
`INTRODUCTION
`
`3
`
`every 6/R seconds. Hence, when the channel bit rate R is fixed, the amount of
`time available to transmit one of the M waveforms corresponding to a b-bit
`sequence is b times the time period in a system that uses binary modulation.
`The communication channel is the physical medium thatis used to send the
`signal
`from the transmitter to the receiver.
`In wireless transmission,
`the
`channel may be the atmosphere (free space). On the other hand, telephone
`channels usually employ a variety of physical media,
`including wire lines,
`optical fiber cables, and wireless (microwave radio), Whatever the physical
`medium used for transmission of the information, the essential feature is that
`the transmitted signal is corrupted in a random mannerbya variety of possibile
`mechanisms, such as additive thermal noise generated by electronic devices,
`man-made noise, ¢.g., automobile ignition notse, and atmospheric noise, e.z.,
`electrical lightning discharges during thunderstorms.
`the digital
`At
`the receiving end of a digital communications system,
`demodulator processes the channel-corrupted transmitted waveform and re-
`duces the waveforms to a sequence of numbers that represent estimates of the
`transmitted data symbols (binary or M-ary). This sequence of numbers is
`passed to the channel decoder, which attempts to reconstruct the original
`information sequence from knowledge of the code used by the channel
`encoder and the redundancy contained in the received data.
`A measure of how well
`the demodulator and decoder perform is the
`frequency with which errors occur in the decoded sequence. Moreprecisely.
`the average probability of a bit-error at the output of the decoder is a measure
`of the performance of the demodulator-decoder combination. In general, the
`probability of error is a function of the code characteristics,
`the types of
`waveforms used to transmit the information over the channel, the transmitter
`power,the characteristics of the channel, i.e., the amount of noise, the nature
`of the interference, etc., and the method of demodulation and decoding. These
`items and their effect on performance will be discussed in detail in subsequent
`chapters.
`As a final step, when an analog output is desired, the source decoder accepts
`the output sequence from the channel decoder and, from knowledge of the
`source encoding method used, attempts to reconstruct the original signal from
`the source. Due to channel decoding errors and possible distortion introduced
`by ihe source encoder and, perhaps, the source decoder, the signal at the
`output of the source decoder is an approximation to the original source output.
`The difference or some function of the difference between the original signal
`and the reconstructedsignal is a measure of the distortion introduced by the
`digital communication system.
`
`1-2 COMMUNICATION CHANNELS AND THEIR
`CHARACTERISTICS
`
`As indicated in the preceding discussion, the communication channel provides
`the connection between the transmitter and the receiver. The physical channel
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`4>piaiTal. COMMUNICATIONS
`
`may be a pair of wires that carry the electrical signal, or an optical fiber that
`carries the information on a modulated light beam, or an underwater ocean
`channelin which the information is transmitted acoustically, or free space over
`which the information-bearing signal is radiated by use of an antenna. Other
`media that can be characterized as communication channels are data storage
`media, such as magnetic tape, magnetic disks, and optical disks.
`One commonprobiem in signal transmission through any channelis additive
`noise. In general, additive noise is generated internally by components such as
`resistors and solid-state devices used to implement the communication system.
`This is sometimescailed thermal noise. Other sources of noise and interference
`may arise externally to the system, such as interference from other users of the
`channel. When such noise and interference occupy the same frequency band as
`the desired signal,
`its effect can be minimized by proper design of the
`transmitted signal and its demodulator at the receiver. Other types of signal
`degradations that may be encountered in transmission over the channel are
`signal attenuation, amplitude and phase distortion, and multipath distortion.
`The effects of noise may be minimized by increasing the power in the
`transmitted signal. However, equipment and other practical constraints limit
`the power level
`in the transmitted signal. Another basic limitation is the
`available channel bandwidth. A bandwidth constraint
`is usually due to the
`physical limitations of the medium and the electronic components used to
`implement the transmitter and the receiver. These two limitations result
`in
`constraining the amount of data that can be transmitted reliably over any
`communications channel as we shail observe in later chapters, Below, we
`describe some of the important characteristics of several communication
`channels.
`
`Wireline Channels The telephone network makes extensive use of wire
`lines for voice signal
`transmission, as well as data and video transmission.
`Twisted-pair wire lines and coaxial cable are basically guided electromagnetic
`channels that provide relatively modest bandwidths. Telephone wire generally
`used to connect a customer to a central office has a bandwidth of several
`hundred kilohertz (kHz). On the other hand, coaxial cable has a usable
`bandwidth of several megahertz (MHz). Figure 1-2-1 illustrates the frequency
`range of guided electromagnetic channels, which include waveguides and
`optical fibers.
`Signals transmitted through such channels are distored in both amplitude
`and phase and further corrupted by additive noise. Twisted-pair wireline
`channels are also prone to crosstalk interference from physically adjacent
`channels, Because wireline channels carry a large percentage of our daily
`communications around the country and the world, much research has been
`performed on the characterization of their transmission properties and on
`methods for mitigating the amplitude and phase distortion encountered in
`signal transmission. In Chapter 9, we describe methods for designing optimum
`transmitted signals and their demodulation;
`in Chapters 10 and 11, we
`
`13
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`13
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`

`

`
`
`
`
`100 mm
`
`C cre
`
`lcm
`
`2
`3
`oF
`Ed
`
`Im
`
`10m
`
`100m
`
`tkn
`
`10 km
`
`100 km
`
`100 GHz
`
`10 GHz
`
`( Giz
`
`100 MHz
`
`10 MHz
`
`| MHz
`
`100 kHz
`
`JO kHz
`
`| kHz _ Frequency range for guided wire channel.
`
`FIGURE1-2-1
`
`consider the design of channel equalizers that compensate for amplitude and
`phase distortion on these channels.
`
`fibers offer the communications system
`Fiber Optic Channels Optical
`designer a channel bandwidth that is several orders of magnitude larger than
`coaxial cable channels. During the past decade, optical fiber cables have been
`developed that have a relatively low signal attenuation, and highly reliable
`photonic devices have been developed for signal generation and signal
`detection. These technological advances have resulted in a rapid deployment of
`optical fiber channels, both in domestic telecommunication systems as well as
`for trans-Atlantic and trans-Pacific communications. With the large bandwidth
`
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`

`
`
`6
`
`DIGITAL COMMUNICATIONS
`
`available on fiber optic channels,it is possible for telephone companiesto offer
`subscribers a wide array of telecommunication services, including voice, data,
`facsimile, and video.
`The transmitter or modulator in a fiber optic communication system is a
`light source, either a light-emitting diode (LED) or a laser. Information is
`transmitted by varying (modulating) the intensity of the light source with the
`message signal, The light propagates through the fiber as a light wave and is
`amplified periodically (in the case of digital transmission, it is detected and
`regenerated by repeaters) along the transmission path to compensate for signal —
`attenuation. At the receiver, the light intensity is detected by a photodiode,
`whose output is an electrical signal
`that varies in direct proportion to the
`powerofthe light impinging on the photodiode. Sources of noise in fiber optic
`channels are photodiodes and electronic amplifiers.
`It is envisioned that optical fiber channels will replace nearly all wireline
`channelsin the telephone network by the turn of the century.
`
`1n wireless communication systems,
`Wireless Electromagnetic Channels
`electromagnetic energy is coupled to the propagation medium by an antenna
`which serves as the radiator, The physical size and the configuration of the
`antenna depend primarily on the frequency of operation. To obtain efficient
`radiation of electromagnetic energy, the antenna must be longer than 3; of the
`wavelength. Consequently, a radio station transmitting in the AM frequency
`band, say at f.= 1 MHz (corresponding to a wavelength of A =c/f. = 300m),
`requires an antenna of at
`least 30m. Other important characteristics and
`altributes of antennas for wireless transmission are described in Chapter5.
`Figure 1-2-2 illustrates the various frequency bands of the electromagnetic
`spectrum. The mode of propagation of electromagnetic waves in the atmo-
`sphere and in free space may be subdivided into three categories, namely,
`ground-wave propagation, sky-wave propagation, and line-of-sight
`(LOS)
`propagation. In the VLF and audio frequency bands, where the wavelengths
`exceed 10 km, the earth and the ionosphere act as a waveguide for electromag-
`netic wave propagation. In these frequency ranges, communication signals
`practically propagate around the globe. For this reason, these frequency bands
`are primarily used to provide navigational aids from shore to ships around the
`world.. The channel bandwidths available in these frequency bands are
`relatively small (usually 1~10% of the center frequency), and hence the
`information that is transmitted through these channels is ofrelatively slow
`speed and generally confined to digital transmission. A dominant type ofnoise
`at these frequencies is generated from thunderstormactivity around the globe,
`especially in tropical regions. Interference results from the manyusers of these
`frequency bands,
`_Ground-waye propagation, as iHustrated in Fig. 1-2-3, is the dominant mode
`of propagation for frequencies in the MF band (0.3-3MHz). This is the
`frequency band used for AM broadcasting and maritime radio broadcasting. In
`AMbroadcasting, the range with groundwave propagation of even the more
`
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`

`

`
`
`Frequency band
`
`Use
`
`CHAPTER |:
`
`INTRODUCTION
`
`7
`
`Experimental
`Navigation
`Satellite to satellite
`Microwaverelay
`Earth-satellite
`Radar
`Mobile radio
`
`VHFTV and FM Broadcast
`Mobile vate
`Business
`Amateur radio
`International radio
`Citizen's band
`
`Very high frequency
`(VHF)
`
`.
`High frequency
`(HF)
`
`Medium frequency
`
`.
`
`
`Visible light
`
` Ulraviolet
`
`
`Millimeter waves
`(EHF)
`
`Super high frequency
`{SHF)
`
`(MF)
`
`1033 Hz
`
`10'* Hz
`
`WO) GHz
`
`10 GHz
`
`1 GHz
`
`100 MHz
`
`10 MHz
`
`ql
`EF
`
`1 MHz
`
`100 kHz
`
`10 kHz
`
`ikHz
`
`Microwave
`radio
`
`Shortwave
`radia
`
`Longwave
`radio
`
`Wavelength
`
`lem
`
`10cm
`
`lkm
`
`10 km
`
`100 km
`
`FIGURE 1-2-2
`
`Frequency range for wireless electromagnetic channels. (Adapted from Carlson (1975),
`2ad edition, © McGraw-Hil! Book Company Co. Reprinted with permission of the publisher}
`
`FIGURE1-2-3__[illustration of ground-wave propagation.
`
`
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`

`

`
`
`8
`
`DIGITAL COMMUNICATIONS
`
` Illustration of sky-wave propagation.
`
`FIGURE 1-2-4
`
`limited to about 150km. Atmospheric noise,
`radio stations is
`powerful
`man-made noise, and thermal noise from electronic components at the receiver
`are dominant disturbances for signal transmission in the MF band.
`Sky-wave propagation, as illustrated in Fig. 1-2-4 results from transmitted
`signals being reflected (bent or refracted) from the ionosphere, which consists
`of several layers of charged particles ranging in altitude from 50 to 400 km
`above the surface of the earth. During the daytime hours, the heating of the
`lower atmosphere by the sun causes the formation of the lower layers at
`altitudes below 120km. These lower layers, especially the D-layer, serve to
`absorb frequencies below 2 MHz, thus severely limiting sky-wave propagation
`of AM radio broadcast. However, during the night-time hours, the electron
`density in the lower layers of the ionosphere drops sharply and the frequency
`absorption that occurs during the daytime is significantly reduced. As a
`consequence, powerful AM radio broadcast stations can propagate over large
`distances via sky wave over the F-layer of the ionosphere, which ranges from
`140 to 400 km above the surface of the earth.
`A frequently occurring problem with electromagnetic wave propagation via
`sky wave in the HF frequency range is signal multipath. Signal multipath occurs
`when the transmitted signal arrives at the receiver via multiple propagation
`paths at different delays. It generally results in intersymbol interference in a
`digital communication system. Moreover, the signal components arriving via
`different propagation paths may add destructively, resulting in a phenomenon
`called signal fading, which most people have experienced whenlistening to a
`distant radio station at night when sky wave is the dominant propagation
`mode. Additive noise at HF is a combination of atmospheric noise and thermal!
`NOISE.
`
`Sky-wave ionospheric propagation ceases to exist at frequencies above
`approximately 30 MHz, which is the end of the HF band. However,it is
`possibie to have ionospheric scatter propagation at frequencies in the range
`30-60 MHz, resulting from signal scattering from the lower ionosphere.It is
`also possible to communicate over distances of several hundred miles by use of
`tropospheric scattering at frequencies in the range 40-300 MHz. Troposcatter
`results from signal scattering due to particles in the atmosphereat altitudes of
`10 miles or
`less. Generally,
`ionospheric scatter and tropospheric scatter
`
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`

`
`
`CHAPTER |:
`
`INTRODUCTION
`
`9
`
`involve jarge signal propagation losses and require a large amount of
`transmitter power andrelatively large antennas.
`Frequencies above 30 MHz propagate through the ionosphere with rela-
`tively littlke
`loss and make satellite and extraterrestrial communications
`possible. Hence, at frequencies in the VHF band and higher, the dominant
`mode of electromagnetic propagation is line-of-sight (LOS) propagation. For
`terrestrial communication systems, this means that the transmitter and receiver
`antennas must be in direct LOS with relatively tittle or no obstruction. For this
`reason,television stations transmilting in the WHF and UHF frequency bands
`mount their antennas on high towers to achieve a broad coverage area.
`In general,
`the coverage area for LOS propagation is limited by the
`curvature of the earth. If the transmitting antenna is mounted at a height Am
`above the surface of the earth, the distance to the radio horizon, assuming no
`physical obstructions such as mountains, is approximately d = V15h km. For
`example, a TV antenna mounted on a tower of 300m in height provides a
`coverage of approximately 67 km. As another example, microwave radio relay
`systems used extensively for telephone and video transmission at frequencies
`above 1 GHz have aniennas mounted on tall
`towers or on the top of tall
`buildings.
`The dominantnoise limiting the performance of a communication system in
`VHF and UHF frequency ranges is thermal noise generated in the receiver
`front end and cosmic noise picked up by the antenna. At frequencies in the
`SHF band above 10 GHz, atmospheric conditions play a major role in signal
`propagation. For example, at 10GHz,
`the attenuation ranges from about
`0,003 dB/km in light rain to about 0.3 dB/km in heavy rain. At 100 GHz, the
`attenuation ranges from about 0.1 dB/km in light rain to about 6dB/km in
`heavy rain. Hence, in this frequency range, heavy rain introduces extremely
`high propag