Principles of Communication System Simulation with Wireless Aplications
【作者】： William H. Tranter K. Sam Shanmugan Theodore S. Rappaport Kurt L. Kosbar
【页数 】：800
【出版社】 ：PRENTICE HALL
【出版日期】：2003
【文件格式】：pdf
CONTENTS
PREFACE xvii
Part I Introduction 1
1 THEROLEOF SIMULATION 1
1.1 Examples of Complexity 2
1.1.1 The Analytically Tractable System 3
1.1.2 The Analytically Tedious System 5
1.1.3 The Analytically Intractable System 7
1.2 Multidisciplinary Aspects of Simulation 8
1.3 Models 11
1.4 Deterministic and Stochastic Simulations 14
1.4.1 An Example of a Deterministic Simulation 16
1.4.2 An Example of a Stochastic Simulation 17
1.5 The Role of Simulation 19
1.5.1 Link Budget and System-Level Specification Process 20
1.5.2 Implementation and Testing of Key Components 22
1.5.3 Completion of the Hardware Prototype and Validation
of the Simulation Model 22
1.5.4 End-of-Life Predictions 22
1.6 Software Packages for Simulation 23
1.7 A Word of Warning 26
1.8 The Use of MATLAB 27
1.9 Outline of the Book 27
1.10 Further Reading 28
2 SIMULATION METHODOLOGY 31
2.1 Introduction 32
2.2 Aspects of Methodology 34
2.2.1 Mapping a Problem into a Simulation Model 34
2.2.2 Modeling of Individual Blocks 41
2.2.3 Random Process Modeling and Simulation 47
2.3 Performance Estimation 49
2.4 Summary 52
2.5 Further Reading 52
2.6 Problems 52
Part II Fundamental Concepts and Techniques 55
3 SAMPLINGANDQUANTIZING 55
3.1 Sampling 56
3.1.1 The Lowpass Sampling Theorem 56
3.1.2 Sampling Lowpass Random Signals 61
3.1.3 Bandpass Sampling 61
3.2 Quantizing 65
3.3 Reconstruction and Interpolation 71
3.3.1 Ideal Reconstruction 71
3.3.2 Upsampling and Downsampling 72
3.4 The Simulation Sampling Frequency 78
3.4.1 General Development 79
3.4.2 Independent Data Symbols 81
3.4.3 Simulation Sampling Frequency 83
3.5 Summary 87
3.6 Further Reading 89
3.7 References 90
3.8 Problems 90
4 LOWPASSSIMULATION MODELS FOR BANDPASS
SIGNALS AND SYSTEMS 95
4.1 The Lowpass Complex Envelope for Bandpass Signals 95
4.1.1 The Complex Envelope: The Time-Domain View 96
4.1.2 The Complex Envelope: The Frequency-Domain View 108
4.1.3 Derivation of Xd(f) and Xq(f) from X (f) 110
4.1.4 Energy and Power 111
4.1.5 Quadrature Models for Random Bandpass Signals 112
4.1.6 Signal-to-Noise Ratios 115
4.2 Linear Bandpass Systems 118
4.2.1 Linear Time-Invariant Systems 118
4.2.2 Derivation of hd(t) and hq(t) from H(f) 122
4.3 Multicarrier Signals 125
4.4 Nonlinear and Time-Varying Systems 128
4.4.1 Nonlinear Systems 128
4.4.2 Time-Varying Systems 130
4.5 Summary 132
4.6 Further Reading 133
4.7 References 134
4.8 Problems 134
4.9 Appendix A: MATLAB Program QAMDEMO 139
4.9.1 Main Program: c4 qamdemo.m 139
4.9.2 Supporting Routines 140
4.10 Appendix B: Proof of Input-Output Relationship 141
5 FILTERMODELSAND SIMULATION TECHNIQUES 143
5.1 Introduction 144
5.2 IIR and FIR Filters 146
5.2.1 IIR Filters 146
5.2.2 FIR Filters 147
5.2.3 Synthesis and Simulation 147
5.3 IIR and FIR Filter Implementations 148
5.3.1 Direct Form II and Transposed Direct
Form II Implementations 148
5.3.2 FIR Filter Implementation 154
5.4 IIR Filters: Synthesis Techniques and Filter Characteristics 155
5.4.1 Impulse-Invariant Filters 155
5.4.2 Step-Invariant Filters 156
5.4.3 Bilinear z-Transform Filters 157
5.4.4 Computer-Aided Design of IIR Digital Filters 165
5.4.5 Error Sources in IIR Filters 167
5.5 FIR Filters: Synthesis Techniques and Filter Characteristics 167
5.5.1 Design from the Amplitude Response 170
5.5.2 Design from the Impulse Response 177
5.5.3 Implementation of FIR Filter Simulation Models 180
5.5.4 Computer-Aided Design of FIR Digital Filters 184
5.5.5 Comments on FIR Design 186
5.6 Summary 186
5.7 Further Reading 189
5.8 References 189
5.9 Problems 190
5.10 Appendix A: Raised Cosine Pulse Example 192
5.10.1 Main program c5 rcosdemo.m 192
5.10.2 Function file c5 rcos.m 192
5.11 Appendix B: Square Root Raised Cosine Pulse Example 193
5.11.1 Main Program c5 sqrcdemo.m 193
5.11.2 Function file c5 sqrc.m 193
5.12 Appendix C: MATLAB Code and Data for Example 5.11 194
5.12.1 c5 FIRFilterExample.m 195
5.12.2 FIR Filter AMP Delay.m 196
5.12.3 shift ifft.m 198
5.12.4 log psd.m 198
6 CASESTUDYHASE-LOCKED LOOPS
AND DIFFERENTIAL EQUATION METHODS 201
6.1 Basic Phase-Locked Loop Concepts 202
6.1.1 PLL Models 204
6.1.2 The Nonlinear Phase Model 206
6.1.3 Nonlinear Model with Complex Input 208
6.1.4 The Linear Model and the Loop Transfer Function 208
6.2 First-Order and Second-Order Loops 210
6.2.1 The First-Order PLL 210
6.2.2 The Second-Order PLL 214
6.3 Case Study: Simulating the PLL 215
6.3.1 The Simulation Architecture 215
6.3.2 The Simulation 216
6.3.3 Simulation Results 219
6.3.4 Error Sources in the Simulation 220
6.4 Solving Differential Equations Using Simulation 223
6.4.1 Simulation Diagrams 224
6.4.2 The PLL Revisited 225
6.5 Summary 230
6.6 Further Reading 231
6.7 References 231
6.8 Problems 232
6.9 Appendix A: PLL Simulation Program 236
6.10 Appendix B: Preprocessor for PLL Example Simulation 237
6.11 Appendix C: PLL Postprocessor 238
6.11.1 Main Program 238
6.11.2 Called Routines 239
6.12 Appendix D: MATLAB Code for Example 6.3 241
7 GENERATING AND PROCESSING RANDOM SIGNALS 243
7.1 Stationary and Ergodic Processes 244
7.2 Uniform Random Number Generators 248
7.2.1 Linear Congruence 248
7.2.2 Testing Random Number Generators 252
7.2.3 Minimum Standards 256
7.2.4 MATLAB Implementation 257
7.2.5 Seed Numbers and Vectors 258
7.3 Mapping Uniform RVs to an Arbitrary pdf 258
7.3.1 The Inverse Transform Method 259
7.3.2 The Histogram Method 264
7.3.3 Rejection Methods 266
7.4 Generating Uncorrelated Gaussian Random Numbers 269
7.4.1 The Sum of Uniforms Method 270
7.4.2 Mapping a Rayleigh RV to a Gaussian RV 273
7.4.3 The Polar Method 275
7.4.4 MATLAB Implementation 276
7.5 Generating Correlated Gaussian Random Numbers 277
7.5.1 Establishing a Given Correlation Coefficient 277
7.5.2 Establishing an Arbitrary PSD
or Autocorrelation Function 278
7.6 Establishing a pdf and a PSD 282
7.7 PN Sequence Generators 283
7.8 Signal Processing 290
7.8.1 Input/Output Means 291
7.8.2 Input/Output Cross-Correlation 291
7.8.3 Output Autocorrelation Function 292
7.8.4 Input/Output Variances 293
7.9 Summary 293
7.10 Further Reading 294
7.11 References 294
7.12 Problems 295
7.13 Appendix A: MATLAB Code for Example 7.11 299
7.14 Main Program: c7 Jakes.m 299
7.14.1 Supporting Routines 300
8 POSTPROCESSING 303
8.1 Basic Graphical Techniques 304
8.1.1 A System Example—π/4 DQPSK Transmission 304
8.1.2 Waveforms, Eye Diagrams, and Scatter Plots 307
8.2 Estimation 309
8.2.1 Histograms 309
8.2.2 Power Spectral Density Estimation 316
8.2.3 Gain, Delay, and Signal-to-Noise Ratios 323
8.3 Coding 329
8.3.1 Analytic Approach to Block Coding 330
8.3.2 Analytic Approach to Convolutional Coding 333
8.4 Summary 336
8.5 Further Reading 336
8.6 References 338
8.7 Problems 339
8.8 Appendix A: MATLAB Code for Example 8.1 342
8.8.1 Main Program: c8 pi4demo.m 342
8.8.2 Supporting Routines 344
9 INTRODUCTION TO MONTE CARLO METHODS 347
9.1 Fundamental Concepts 347
9.1.1 Relative Frequency 348
9.1.2 Unbiased and Consistent Estimators 349
9.1.3 Monte Carlo Estimation 349
9.1.4 The Estimation of π 351
9.2 Application to Communications Systems—The AWGN Channel 354
9.2.1 The Binomial Distribution 355
9.2.2 Two Simple Monte Carlo Simulations 359
9.3 Monte Carlo Integration 366
9.3.1 Basic Concepts 368
9.3.2 Convergence 370
9.3.3 Confidence Intervals 371
9.4 Summary 375
9.5 Further Reading 375
9.6 References 375
9.7 Problems 376
10 MONTE CARLO SIMULATION
OF COMMUNICATION SYSTEMS 379
10.1 Two Monte Carlo Examples 380
10.2 Semianalytic Techniques 393
10.2.1 Basic Considerations 394
10.2.2 Equivalent Noise Sources 397
10.2.3 Semianalytic BER Estimation for PSK 398
10.2.4 Semianalytic BER Estimation for QPSK 400
10.2.5 Choice of Data Sequence 404
10.3 Summary 405
10.4 References 406
10.5 Problems 406
10.6 Appendix A: Simulation Code for Example 10.1 408
10.6.1 Main Program 408
10.6.2 Supporting Program: random binary.m 409
10.7 Appendix B: Simulation Code for Example 10.2 410
10.7.1 Main Program 410
10.7.2 Supporting Programs 414
10.7.3 vxcorr.m 414
10.8 Appendix C: Simulation Code for Example 10.3 415
10.8.1 Main Program: c10 PSKSA.m 415
10.8.2 Supporting Programs 416
10.9 Appendix D: Simulation Code for Example 10.4 418
10.9.1 Supporting Programs 419
11 METHODOLOGY FOR SIMULATING
A WIRELESS SYSTEM 421
11.1 System-Level Simplifications and Sampling Rate Considerations 423
11.2 Overall Methodology 424
11.2.1 Methodology for Simulation of the Analog Portion
of the System 429
11.2.2 Summary of Methodology for Simulating
the Analog Portion of the System 441
11.2.3 Estimation of the Coded BER 441
11.2.4 Estimation of Voice-Quality Metric 441
11.2.5 Summary of Overall Methodology 442
11.3 Summary 443
11.4 Further Reading 443
11.5 References 444
11.6 Problems 444
Part III Advanced Models and Simulation Techniques 447
12 MODELING AND SIMULATION OF NONLINEARITIES 447
12.1 Introduction 448
12.1.1 Types of Nonlinearities and Models 448
12.1.2 Simulation of Nonlinearities—Factors to Consider 449
12.2 Modeling and Simulation of Memoryless Nonlinearities 451
12.2.1 Baseband Nonlinearities 452
12.2.2 Bandpass Nonlinearities—Zonal Bandpass Model 453
12.2.3 Lowpass Complex Envelope
(AM-to-AM and AM-to-PM) Models 455
12.2.4 Simulation of Complex Envelope Models 461
12.2.5 The Multicarrier Case 462
12.3 Modeling and Simulation of Nonlinearities with Memory 468
12.3.1 Empirical Models Based on Swept Tone Measurements 470
12.3.2 Other Models 472
12.4 Techniques for Solving Nonlinear Differential Equations 475
12.4.1 State Vector Form of the NLDE 476
12.4.2 Recursive Solutions of NLDE-Scalar Case 479
12.4.3 General Form of Multistep Methods 483
12.4.4 Accuracy and Stability of Numerical Integration Methods 483
12.4.5 Solution of Higher-Order NLDE-Vector Case 485
12.5 PLL Example 486
12.5.1 Integration Methods 486
12.6 Summary 488
12.7 Further Reading 488
12.8 References 489
12.9 Problems 490
12.10 Appendix A: Saleh’s Model 493
12.11 Appendix B: MATLAB Code for Example 12.2 494
12.11.1 Supporting Routines 495
13 MODELING AND SIMULATION
OF TIME-VARYING SYSTEMS 497
13.1 Introduction 497
13.1.1 Examples of Time-Varying Systems 498
13.1.2 Modeling and Simulation Approach 499
13.2 Models for LTV Systems 500
13.2.1 Time-Domain Description for LTV System 500
13.2.2 Frequency Domain Description of LTV Systems 503
13.2.3 Properties of LTV Systems 505
13.3 Random Process Models 511
13.4 Simulation Models for LTV Systems 515
13.4.1 Tapped Delay Line Model 515
13.5 MATLAB Examples 518
13.5.1 MATLAB Example 1 518
13.5.2 MATLAB Example 2 520
13.6 Summary 522
13.7 Further Reading 523
13.8 References 523
13.9 Problems 523
13.10 Appendix A: Code for MATLAB Example 1 525
13.10.1 Supporting Program 526
13.11 Appendix B: Code for MATLAB Example 2 527
13.11.1 Supporting Routines 528
13.11.2 mpsk pulses.m 528
14 MODELING AND SIMULATION
OF WAVEFORM CHANNELS 529
14.1 Introduction 529
14.1.1 Models of Communication Channels 530
14.1.2 Simulation of Communication Channels 531
14.1.3 Discrete Channel Models 532
14.1.4 Methodology for Simulating Communication
System Performance 532
14.1.5 Outline of Chapter 533
14.2 Wired and Guided Wave Channels 533
14.3 Radio Channels 534
14.3.1 Tropospheric Channel 536
14.3.2 Rain Effects on Radio Channels 537
14.4 Multipath Fading Channels 538
14.4.1 Introduction 538
14.4.2 Example of a Multipath Fading Channel 538
14.4.3 Discrete Versus Diffused Multipath 545
14.5 Modeling Multipath Fading Channels 546
14.6 Random Process Models 547
14.6.1 Models for Temporal Variations
14.6.2 Important Parameters 550
14.7 Simulation Methodology 552
14.7.1 Simulation of Diffused Multipath Fading Channels 553
14.7.2 Simulation of Discrete Multipath Fading Channels 558
14.7.3 Examples of Discrete Multipath Fading Channel Models 565
14.7.4 Models for Indoor Wireless Channels 571
14.8 Summary 571
14.9 Further Reading 572
14.10 References 572
14.11 Problems 575
14.12 Appendix A: MATLAB Code for Example 14.1 577
14.12.1 Main Program 577
14.12.2 Supporting Functions 578
14.13 Appendix B: MATLAB Code for Example 14.2 580
14.13.1 Main Program 580
14.13.2 Supporting Functions 581
15 DISCRETE CHANNEL MODELS 583
15.1 Introduction 584
15.2 Discrete Memoryless Channel Models 586
15.3 Markov Models for Discrete Channels with Memory 589
15.3.1 Two-State Model 589
15.3.2 N-state Markov Model 596
15.3.3 First-Order Markov Process 597
15.3.4 Stationarity 597
15.3.5 Simulation of the Markov Model 598
15.4 Example HMMs—Gilbert and Fritchman Models 601
15.5 Estimation of Markov Model Parameters 604
15.5.1 Scaling 611
15.5.2 Convergence and Stopping Criteria 612
15.5.3 Block Equivalent Markov Models 613
15.6 Two Examples 615
15.7 Summary 621
15.8 Further Reading 622
15.9 References 622
15.10 Problems 623
15.11 Appendix A: Error Vector Generation 627
15.11.1 Program: c15 errvector.m 627
15.11.2 Program: c15 hmmtest.m 628
15.12 Appendix B: The Baum-Welch Algorithm 629
15.13 Appendix C: The Semi-Hidden Markov Model 632
15.14 Appendix D: Run-Length Code Generation 636
15.15 Appendix E: Determination of Error-Free Distribution 637
15.15.1 c15 intervals1.m 637
15.15.2 c15 intervals2.m 637
16 EFFICIENT SIMULATION TECHNIQUES 639
16.1 Tail Extrapolation 640
16.2 pdf Estimators 642
16.3 Importance Sampling 645
16.3.1 Area of an Ellipse 646
16.3.2 Sensitivity to the pdf 655
16.3.3 A Final Twist 656
16.3.4 The Communication Problem 657
16.3.5 Conventional and Improved Importance Sampling 659
16.4 Summary 660
16.5 Further Reading 660
16.6 References 662
16.7 Problems 662
16.8 Appendix A: MATLAB Code for Example 16.3 665
16.8.1 Supporting Routines 669
17 CASE STUDY: SIMULATION
OF A CELLULAR RADIO SYSTEM 671
17.1 Introduction 671
17.2 Cellular Radio System 673
17.2.1 System-Level Description 673
17.2.2 Modeling a Cellular Communication System 676
17.3 Simulation Methodology 688
17.3.1 The Simulation 688
17.3.2 Processing the Simulation Results 700
17.4 Summary 706
17.5 Further Reading 706
17.6 References 707
17.7 Problems 708
17.8 Appendix A: Program for Generating the Erlang B Chart 710
17.9 Appendix B: Initialization Code for Simulation 712
17.10 Appendix C: Modeling Co-Channel Interference 714
17.10.1 Wilkinson’s Method 715
17.10.2 Schwartz and Yeh’s Method 717
17.11 Appendix D: MATLAB Code for Wilkinson’s Method 718
18 TWO EXAMPLE SIMULATIONS 719
18.1 A Code-Division Multiple Access System 720
18.1.1 The System 720
18.1.2 The Simulation Program 724
18.1.3 Example Simulations 726
18.1.4 Development of Markov Models 729
18.2 FDM System with a Nonlinear Satellite Transponder 734
18.2.1 System Description and Simulation Objectives 734
18.2.2 The Overall Simulation Model 737
18.2.3 Uplink FDM Signal Generation 738
18.2.4 Satellite Transponder Model 740
18.2.5 Receiver Model and Semianalytic BER Estimator 741
18.2.6 Simulation Results 742
18.2.7 Summary and Conclusions 744
18.3 References 746
18.4 Appendix A: MATLAB Code for CDMA Example 747
18.4.1 Supporting Functions 750
18.5 Appendix B: Preprocessors for CDMA Application 753
18.5.1 Validation Run 753
18.5.2 Study Illustrating the Effect of the Ricean K-Factor 753
18.6 Appendix C: MATLAB Function c18 errvector.m 755
18.7 Appendix D: MATLAB Code for Satellite FDM Example 756
18.7.1 Supporting Functions 760
