B. P. Lathi, Zhi Ding - Modern Digital and Analog Communication Systems-Oxford University Press (2009)

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7PRINCIPLES OF DIGITAL DATATRANSMISSION 3267.1 DIGITAL COMMUNICATION SYSTEMS 3267.1. l Source 3267.1.2 Line Coder 3277.1.3 Multiplexer 3287.1.4 Regenerative Repeater 3287.2 LINE CODING 3297.2. l PSD of Various Line Codes 3307.2.2 Polar Signaling 3347.2.3 Constructing a DC Null in PSD by PulseShaping 3367.2.4 On-Off Signaling 3377.2.5 Bipolar Signaling 3397.3 PULSE SHAPING 3437.3. l lntersymbol Interferences (ISi) and Effect 3437.3.2 Nyquist's First Criterion for Zero ISi 3447.3.3 Controlled ISi or Partial Response Signaling 3507.3.4 Example of a Duobinary Pulse 3517.3.5 Pulse Relationship between Zero-ISi, Duobinary, andModified Duobinary 3527.3.6 Detection of Duobinary Signaling and DifferentialEncoding 3537.3.7 Pulse Generation 3557.4 SCRAMBLING 3557.5 DIGITAL RECEIVERS AND REGENERATIVE REPEATERS 3587.5. l Equalizers 3597.5.2 Timing Extraction 3637.5.3 Detection Error 3657.6 EYE DIAGRAMS: AN IMPORTANT TOOL 3667.7 PAM: MARY BASEBAND SIGNALING FOR HIGHERDATA RATE 3697.8 DIGITAL CARRIER SYSTEMS 3727.8. l Basic Binary Carrier Modulations 3727.8.2 PSD of Digital Carrier Modulation 3747.8.3 Connections between Analog and Digital CarrierModulations 3767.8.4 Demodulation 3777. 9 MARY DIGITAL CARRIER MODULATION 3807.10 MATLAB EXERCISES 386Contentsxi
xiiCONTENTS8FUNDAMENTALS OF PROBABILITYTHEORY 3938.1 CONCEPT OF PROBABILITY 3938.2 RANDOM VARIABLES 4088.3 STATISTICAL AVERAGES (MEANS) 4278.4 CORRELATION 4368.5 LINEAR MEAN SQUARE ESTIMATION 4408.6 SUM OF RANDOM VARIABLES 4438.7 CENTRAL LIMIT THEOREM 446910RANDOM PROCESSES AND SPECTRALANALYSIS 4569.1 FROM RANDOM VARIABLE TO RANDOM PROCESS 4569.2 CLASSIFICATION OF RANDOM PROCESSES 4619.3 POWER SPECTRAL DENSITY 4659.4 MULTIPLE RANDOM PROCESSES 4799.5 TRANSMISSION OF RANDOM PROCESSES THROUGH LINEARSYSTEMS 4809.6 APPLICATION: OPTIMUM FILTERING (WIENER-HOPF FILTER) 4839.7 APPLICATION: PERFORMANCE ANALYSIS OF BASEBAND ANALOGSYSTEMS 4869.8 APPLICATION: OPTIMUM PREEMPHASIS-DEEMPHASISSYSTEMS 4889.9 BANDPASS RANDOM PROCESSES 491PERFORMANCE ANALYSIS OF DIGITALCOMMUNICATION SYSTEMS 50610.1 OPTIMUM LINEAR DETECTOR FOR BINARY POLARSIGNALING 50610.1.1 Binary Threshold Detection 50710.1 .2 Optimum Receiver Filter-Matched Filter 50810.2 GENERAL BINARY SIGNALING 51210.2. 1 Optimum Linear Receiver Analysis 51 210.2.2 Performance Analysis of General Binary Systems 516
Page 1 and 2: INTERNATIONAL FOURTH EDITIONModern
Page 3 and 4: Oxford University Press, Inc., publ
Page 6 and 7: CONTENTSPREFACE xvii1INTRODUCTION1
Page 8 and 9: Contents1x3 .6 SIGNAL DISTORTION OV
Page 12 and 13: Contentsxiii1 0.3 COHERENT RECEIVER
Page 14 and 15: Contentsxv13.3 ERROR-FREE COMMUNICA
Page 16 and 17: PREFACE (INTERNATIONALEDITION)The c
Page 18 and 19: Preface (International Edition)xixM
Page 20: Preface (International Edition)xx,m
Page 24 and 25: l INTRODUCTIONOver the past decade,
Page 26 and 27: 1 . 1 Communication Systems 3Figure
Page 28 and 29: 1 .2 Analog and Di gital Messag es
Page 30 and 31: l .2 Analog and Digital Messages 7H
Page 32 and 33: 1 .3 Channel Effect, Signal-to-Nois
Page 34 and 35: 1 .4 Modulation and Detection 11In
Page 36 and 37: 1.5 Digital Source Coding and Error
Page 38 and 39: 1.6 A Brief Historical Review of Mo
Page 40 and 41: 1.6 A Brief Historical Review of Mo
Page 42 and 43: References 19to transmit the tones
Page 44 and 45: 2.1 Size of a Signal 21Figure 2.1Ex
Page 46 and 47: 2.2 Classification of Signals 233.
Page 48 and 49: 2.2 Classification of Signals 25Fig
Page 50 and 51: 2.3 Unit Impulse Signal 27Figure 2.
Page 52 and 53: 2.4 Signals Versus Vectors 29Fi gur
Page 54 and 55: 2 .4 Signals Versus Vectors 31andf/
Page 56 and 57: 2.4 Signals Versus Vectors 33functi
Page 58 and 59: 2 .5 Correlation of Signals 35Thus,
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2.6 Orthogonal Signal Set 37orthogo
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2 .7 The Exponential Fourier Series
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2 .7 The Exponential Fourier Series
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2.7 The Exponential Fourier Series
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2.7 The Exponential Fourier Series
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2.8 MATLAB Exercises 47Basic Signal
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2.8 MATLAB Exercises 49% (file name
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2.8 MATLAB Exercises 51subplot (231
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2.8 MATLAB Exercises53Figure 2.22Ex
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Problems 55Figure P.2. 1-2x(t)y(t):
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Problems 57Figure P.2.3-3g(t)t -han
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Problems 592.5-1 Find the correlati
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Problems 61(b) Determine the odd an
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3 .1 Aperiodic Signal Representatio
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3.1 Aperiodic Signal Representation
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3 .1 Aperiodic Signal Representatio
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3.2 Transforms of Some Useful Funct
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3.3 Some Properties of the Fourier
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3 .3 .5 Frequency-Shifting Property
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3 .3 Some Properties of the Fourier
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Here D n = I/To. Therefore, from Eq
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TABLE 3.2Properties of Fourier Tran
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3.4 Signal Transmission Through a L
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3.5 Ideal versus Practical Filters
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3.6 Signal Distortion Over a Commun
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3 .7 Signal Energy and Energy Spect
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Figure 3.32Interpretation ofthe ene
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3.7 Signal Energy and Energy Spectr
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3.7 Signal Energy and Energy Spectr
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3.8 Signal Power and Power Spectral
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3.9 Numerical Computation of Fourie
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3.9 Numerical Computation of Fourie
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3.10 MATLAB Exercises 123Using the
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3 . 10 MATLAB Exercises 125In this
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3. 10 MATLAB Exercises 127Observe t
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3.10 MATLAB Exercises 129We multipl
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This is the trigonometric form of t
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3.3-2 The Fourier transform of the
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Problems 1353.3-7 Use the frequency
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Problems 137(b) Discuss how this ch
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Problems 139the autocorrelation fun
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4.1 Baseband Ve rsus Carrier Commun
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4.2 Double-Sideband Amplitude Modul
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4.3 Amplitude Modulation (AM) 151Th
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4.3 Amplitude Modulation (AM) 153Fi
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4.3 Amplitude Modulation (AM) 155In
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4.3 Amplitude Modulation (AM) 157Fi
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either utilize or remove the 100% s
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4.4 Bandwidth-Efficient Amplitude M
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4.5 Amplitude Modulations: Vestigia
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4.5 Amplitude Modulations: Vestigia
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4.6 Local Carrier Synchronization 1
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4.8 Phase-locked Loop and Some Appl
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4. 9 MATLAB Exercises 181whereE(t)
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4. 9 MATLAB Exercises 183Fi g ure 4
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4. 9 MATLAB Exercises 185s_dem=s_ds
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4. 9 MATLAB Exercises187Figure 4.35
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4.9 MATLAB Exercises189Figure 4.36
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4. 9 MATLAB Exercises 191subplot (2
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Figure 4.41Frequencydomain signalsd
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title ('second demodulator output '
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Problems 1974.2-5 You are asked to
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Problems 1994.3-3 For the AM signal
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Problems 201Figure P.4.4·6'PL5B (t
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5.1 Nonlinear Modulation 203the AM
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Figure 5.2Phase andfrequencymodulat
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5.1 Nonlinear Modulation 207Dividin
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5.2 Bandwidth of Angle-Modulated Wa
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Alternately, the deviation ratio f3
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5.3 Generating FM Waves 223Figure 5
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5.3 Generating FM Waves 225Indirect
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5.3 Generating FM Waves 227Amplitud
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5.3 Generating FM Waves 229We may a
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5.4 Demodulation of FM Signals 23 1
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5.4 Demodulation of FM Signals 233B
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5.5 Effects of Nonlinear Distortion
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5.5 Effects of Nonlinear Distortion
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5.6 Superheterodyne Analog AM/FM Re
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5.7 FM BROADCASTING SYSTEM5.7 FM Br
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5.8 MATLAB Exercises 243Figure 5.19
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5.8 MATLAB Exercises 245title ('PM
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Problems 2476. H. R. Slotten, '"Rai
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Problems 249Figure P.5.4-15.4-1 (a)
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6SAMPLING ANDANALOG-TO-DIGITALCONVE
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6. 1 Sampling Theorem 253by nfs . T
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6.1 Sampling Theorem 255identical t
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6. 1 Sampling Theorem 257Figure 6.5
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6.1 Sampling Theorem 259Figure 6.7S
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6. 1 Sampling Theorem 261known as s
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6.1 Sampling Theorem 263Figure 6.9(
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6. l Sampling Theorem 265whereq T s
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6.1 Sampling Theorem 267Moreover, t
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6.2 Pulse Code Modulation (PCM) 269
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6.2 Pulse Code Modulation {PCM) 273
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6.3 Digital Telephony: PCM in Tl Ca
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6.3 Digital Telephony: PCM in Tl Ca
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6.4 DIGITAL MULTIPLEXING6.4 Digital
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6.4 Digital Multiplexing 287Figure
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6.4 Digital Multiplexing 289Figure
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6.5 Differential Pulse Code Modulat
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6.5 Differential Pulse Code Modulat
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6.7 Delta Modulation 295deploy. Unl
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6.7 Delta Modulation 297Figure 6.31
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6.7 Delta Modulation 299Figure 6.32
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6.8 Vocoders and Video Compression
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6.9 MATLAB Exercises 31 1Fi gure 6.
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6. 9 MATLAB Exercises 315% ts new s
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t= [O:td :1.]; %time interval of 1
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PROBLEMSFigure P.6. 1 - 1Problems 3
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Problems 323(c) This filter, being
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Problems 325the transmission bandwi
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7. 1 Digital Communication Systems
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7.2 Line Coding 329Figure 7.3An on-
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7.2 Line Coding 33 1in Fig. 7 .4b,
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7.2 Line Coding 333and from Eq. (7.
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7.2 Line Coding 335Moreover, both G
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7.2 Line Coding 337Figure 7.7Split-
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7.2 Line Coding 339Fi gure 7.8Power
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7.2 Line Coding 341Figure 7.9PSD of
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7.3 Pulse Shaping 343Binary with N
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7.3 Pulse Shaping 345to be positive
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7.3 Pulse Shaping 347Fi g ure 7. 13
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7.3 Pulse Shaping 349the case of th
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TABLE 7. 1Transmitted Bits and the
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7.3 Pulse Shaping 353In fact, many
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7.4 Scrambling 355TABLE 7.2Binary D
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7.4 Scrambling 357Figure 7.20 shows
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Figure 7.21Regenerativerepeater.7.5
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7 .5 Digital Receivers and Regenera
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7.5 Digital Receivers and Regenerat
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7.5 Digital Receivers and Regenerat
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7.6 Eye Diagrams: An Important Tool
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7.7 Pam: M-Ary Baseband Signaling f
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7.7 Pam: M-Ary Baseband Signaling f
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Figure 7.30(a) The carriercos Wet.
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where 'VT (f) is the Fourier transf
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7.8 Digital Carrier Systems 377we c
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7. 8 Digital Carrier Systems 379Fi
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7.9 MAry Digital Carrier Modulation
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7.9 M-Ary Digital Carrier Modulatio
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7.9 M-Ary Digital Carrier Modulatio
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7. l O MATLAB Exercises 387The firs
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Problems 389(a) Assuming half-width
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Problems 39 1sequence T is applied
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8FUNDAMENTALS OFPROBABILITY THEORYT
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8.1 Concept of Probability 395Figur
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8.1 Concept of Probability 397Exam
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8.1 Concept of Probability 399Let t
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8.1 Concept of Probability 401But t
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8.1 Concept of Probability 403Hence
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8.1 Concept of Probability 405Figur
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8.1 Concept of Probability 407IP(F
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8.2 Random Va riables 409Figure 8.5
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8 .2 Random Variables 41 1Similarly
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8.2 Random Variables 413Because x :
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8.2 Random Va riables 415From Eq. (
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8.2 Random Variables 417This integr
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8.2 Random Variables 419TABLE 8.2Co
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8.2 Random Va riables 421Figure 8.1
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8.2 Random Variables 423Figure 8.13
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8.2 Random Va riables 425Independen
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8.3 Statistical Averages (Means) 42
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8.4 Correlation 437RVs x and y. We
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8.4 Correlation 439Thus, if x and y
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Since by definition xy = R x y and
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8.6 Sum of Random Va riables 443Fig
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Upon carrying out this convolution
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8.7 Central Limit Theorem 447is kno
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PROBLEMSProblems4498.1-1 A card is
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Problems 45 18.1-16 In a binary com
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Problems 453where M = ab - c 2 . Sh
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Problems 4558.6-3 If x(t) and y(t)
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9. 1 From Random Va riable to Rando
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9. 1 From Random Va riable to Rando
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9 .2 Classification of Random Proce
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Because pe(0) = 1/2n over (0, 2n) a
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9 .3 Power Spectral Density 465resp
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9.3 Power Spectral Density 467Figur
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9.3 Power Spectral Density 469It is
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9.3 Power Spectral Density 471Hence
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9.3 Power Spectral Density 473where
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9.3 Power Spectral Density 475Recal
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In each case we shall first determi
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9.4 MULTIPLE RANDOM PROCESSES9 .4 M
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9.5 Transmission of Random Processe
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9.6 Application: Optimum Filtering
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9.6 Application: Optimum Filtering
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9.7 Application: Performance Analys
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9.8 Application: Optimum Preemphasi
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9.9 Bandpass Random Processes 491an
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9.9 Bandpass Random Processes 493Th
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It follows from this equation and f
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9.9 Bandpass Random Processes 497Ba
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References 499Figure 9.26Rican PDF.
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Problems 5019.2-1 For each of the f
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Problems 503by first finding its tr
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Problems 5059.8-1A white process of
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10.1 Optimum Linear Detector for Bi
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10. 1 Optimum Linear Detector for B
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10.1 Optimum Linear Detector for Bi
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10.2 General Binary Signaling 513Fi
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10.2 General Binary Signaling 515Th
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10.2 General Binary Signaling 517Fi
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Additionally, substituting q(t) = 0
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where the pulse energy is simplyl 0
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To compute P b from Eq. (10.25b), w
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10.4 Signal Space Analysis of Optim
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10.5 Vector Decomposition of White
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10.6 Optimum Receiver for White Gau
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l 0.6 Optimum Receiver for White Ga
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Figure 10.21Determiningoptimumdecis
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(M - 2) symbols. Hence,10.6 Optimum
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10.7 General Expression for Error P
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10.8 Equivalent Signal Sets 569and
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10.8 Equivalent Signal Sets 571Assu
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10.8 Equivalent Signal Sets 573Obse
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10.8 Equivalent Signal Sets 575As a
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10. 9 Nonwhite (Colored) Channel No
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l 0. 10 Other Useful Performance Cr
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10.1 1 Noncoherent Detection 581err
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10.1 1 Noncoherent Detection 583Bec
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10.1 1 Noncoherent Detection 585Fig
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10.1 1 Noncoherent Detection 587Sub
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Figure 10.42Error probability P Pof
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l 0.12 MATLAB Exercises 591yrect=xr
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Figure 1 0.45Power spectraldensity
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10.12 MATLAB Exercises 595set (figw
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10.12 MATLAB Exercises 597Figure 1
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10.12 MATLAB Exercises 601noiseq=ra
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Problems 603legend ('Analytical BER
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Problems 605where Am sin wet is the
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10.4-4 For the three basis signals
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FigureP. 10.6·5r--a• "v • •s
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Problems 61 110. 7-1 The vertices o
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Problems 613Hint: Use Gram-Schmidt
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11 . 1 Frequency Hopping Spread Spe
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11.1 Frequency Hopping Spread Spect
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11 .2 Multiple FHSS User Systems an
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l l .3 APPLICATIONS OF FHSS11 .3 Ap
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11.3 Applications of FHSS 623To com
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l l .4 Direct Sequence Spread Spect
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11 .4 Direct Sequence Spread Spectr
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11 .5 Resilient Features of DSSS 62
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11.6 Code Division Multiple-Access
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11 .6 Code Division Multiple-Access
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11 .6 Code Division Multiple-Access
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11 .7 Multiuser Detection (MUD) 637
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11. 8 Modern Practical DSSS CDMA Sy
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l l .8 Modern Practical DSSS CDMA S
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11.8 Modern Practical DSSS CDMA Sys
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11. 8 Modern Practical DSSS CDMA Sy
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11 . 9 MATLAB Exercises 651Figure 1
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11 . 9 MATLAB Exercises 653% can be
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11.9 MATLAB Exercises 655% Add jamm
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11.9 MATLAB Exercises 657The first
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11.9 MATLAB Exercises 659y_out=x_in
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11 . 9 MATLAB Exercises 661Eb2N_num
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References 663awgnois=signois*noise
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Problems 665(a) Find the probabilit
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12.1 Linear Distortions of Wireless
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12.1 Linear Distortions of Wireless
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12.2 Receiver Channel Equalization
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12.3 Linear T-Spaced Equalization (
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Minimum MSE and Optimum DelayBecaus
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12.4 Linear Fractionally Spaced Equ
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12.4 Linear Fractionally Spaced Equ
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12.6 Decision Feedback Equalizer 68
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12.6 Decision Feedback Equalizer 69
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12.7 OFDM (Multicarrier) Communicat
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h[0] h[l] h[L] 0 012.7 OFDM (Multic
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X __!:_ [ :Nl1H[O]H[O]=N __!:_ :H[O
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Figure 12. 13DMT transmissionof Ndi
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12.8 Discrete Multitone (DMT) Modul
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12.9 Real-Life Applications of OFDM
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Digital Broadcasting12.9 Real-Life
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12.10 Blind Equalization and Identi
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the maximum Doppler shift is bounde
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12.12 MATLAB Exercises 715is known
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12.12 MATLAB Exercises 717% Generat
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12.12 MATLAB Exercises 719After a m
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Fi g ure 12.22Scatter plots ofsigna
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12.12 MATLAB Exercises 723% It show
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12.12 MATLAB Exercises 725Fi g ure
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References 729Figure 1 2.28Average
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PROBLEMSProblems 73 112.1-1 In a QA
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Problems 73312.7-3 Consider an FIR
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13. l Measure of Information 735The
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13. 1 Measure of Information 737Hen
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13.2 Source Encoding 739on the aver
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13.2 Source Encoding 74 1TABLE 13.1
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13.2 Source Encoding 743TABLE 13.3O
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13.3 Error-Free Communication Over
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13.3 Error-Free Communication Over
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13.4 Channel Capacity of a Discrete
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13.5 Channel Capacity of a Continuo
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subject to the following constraint
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13.5 Channel Capacity of a Conti nu
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13.5 Channel Capacity of a Conti nu
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13.6 Practical Communication System
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13.6 Practical Communication System
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13 .7 Frequency-Selective Channel C
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13.7 Frequency-Selective Channel Ca
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13.8 MULTIPLE-INPUT-MULTIPLE-OUTPUT
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13.8 Multiple-Input-Multiple-Output
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1 3. 9 MATLAB Exercises 789Fi gure
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% probabilities of each source inpu
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13.9 MATLAB Exercises 793estimate=e
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13.9 MATLAB Exercises 795% Matlab P
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Problems 7973. H. Nyquist, "Certain
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Problems 79913.2-3 A source emits o
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Problems 80113.4-5 A cascade of two
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14.2 Redundancy for Error Correctio
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14.2 Redundancy for Error Correctio
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14.3 Linear Block Codes 807codeword
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14.3 Linear Block Codes 809where th
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14.3 Linear Block Codes 81 1TABLE 1
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14.4 Cyclic Codes 813This is precis
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14.4 Cyclic Codes 815Regardless of
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14.4 Cyclic Codes 817Example 1 4.4
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Consider a Hamming (7, 4, 3) code w
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14.4 Cyclic Codes 821TABLE 14.6e100
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TABLE 14.7Commonly Used CRC Codes a
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Figure 14.3Performancecomparison of
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14.6 Convolutional Codes 827check d
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14.6 Convolutional Codes 829Figure
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14.6 Convolutional Codes 83 1Fi gur
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Figure 14, 10 !Iii! Received bits:
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Figure 14. 1 1Convolutionalencoder.
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14.7 Trellis Diagram of Block Codes
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14.8 CODE COMBINING AND INTERLEAVIN
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14.9 Soft Decoding 841Figure 14. 1
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14.9 Soft Decoding 843modify the ha
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14. 10 Soft-Output Viterbi Algorith
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14.1 1 Turbo Codes 847following sim
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14.1 1 Turbo Codes 849derived from
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14.1 1 Turbo Codes 851Figure 14.20
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14. 1 1 Turbo Codes 853whereas from
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14. 1 2 Low-Density Parity Check (L
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14. 12 Low-Density Parity Check (LD
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14. 12 Low-Density Parity Check (LD
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14. 13 MATLAB EXERCISES14. 1 3 MATL
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14.13 MATLAB Exercises 863Exl4 3r =
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References 865rig_l= (l+sign ( xig_
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Problems 867generates a ( 4,2) code
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Problems 869Hint: A third-order pol
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Problems 871FigureP. 14.5-2(b) Use
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APPENDIX AORTHOGONALITY OF SOME SIG
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APPENDIX BCAUCHY-SCHWARZ INEQUALITY
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APPENDIX CGRAM-SCHMIDT ORTHOGONALIZ
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Appendix C: Gram-Schmidt Orthogonal
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Appendix D: Basic Matrix Properties
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Appendix D: Basic Matrix Properties
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APPENDIX EMISCELLANEOUSE.1 L'Hopita
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1cos 3 x = (3 cosx4 + cos 3x)1sin 3
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IND EXAdaptive delta modulation (AD
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Index 891of discrete memoryless cha
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Index 893Energyof modulated signals
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Index 895Ideal vs. practical filter
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Index 897discrete multitone (DMT),
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Index 899of digital carrier modulat
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Index 90 1transmitter power loading
Page 926:
Index 903vector decomposition of ra
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B. P. Lathi, Zhi Ding - Modern Digital and Analog Communication Systems-Oxford University Press (2009)

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