Principles of Communication Systems Simulation With Wireless Applications

by Tranter, William H.; Shanmugan, K. Sam; Rappaport, Theodore S.; Kosbar, Kurt L.

• ISBN13: 9780134947907
• ISBN10: 0134947908
• Edition: 1st
• Format: Paperback
• Copyright: 2003-12-30
• Publisher: Prentice Hall

Summary

Written by some of the world's leaders in communications, this is a hands-on, example-rich guide to simulating communications systems. The book offers practical guidance on every key aspect of simulation, as well as detailed models that can serve as laboratories for evaluating the potential impact of changes in system design.

Author Biography

WILLIAM H. TRANTER is Bradley Professor of Communications and Associate Director of the Mobile and Portable Radio Research Group at Virginia Tech. He has authored or co-authored many technical papers and two widely used textbooks: Principles of Communications: Systems, Modeling, and Noise, Fifth Edition and Signals and Systems: Continuous and Discrete, Fourth Edition. He is a Fellow of the IEEE and currently serves as Vice President - Technical Activities of the IEEE Communications Society.

K. SAM SHANMUGAN is Southwestern Bell Distinguished Professor of Electrical Engineering and Computer Science at The University of Kansas, Lawrence. He is a Fellow of the IEEE and author or co-author of over 100 publications and three books: Digital and Analog Communication Systems, Random Signals and Noise, and Simulation of Communication Systems, Second Edition.

THEODORE S. RAPPAPORT is a professor of Electrical and Computer Engineering at the University of Texas, and director of the Wireless Networking and Communications Group (WNCG.org). In 1990, he founded the Mobile and Portable Radio Research Group (MPRG) at Virginia Tech, one of the first university research and educational programs for the wireless communications field. He is the editor or co-editor of four other books on the topic of wireless communications, based on his teaching and research activities at MPRG.

KURT L. KOSBAR is Assistant Chair for Laboratories and Equipment, Department of Electrical and Computer Engineering, University of Missouri, Rolla.

Table of Contents

PREFACE	xvii
Part I Introduction	1	(54)
1 THE ROLE OF SIMULATION	1	(30)
1.1 Examples of Complexity	2	(6)
1.1.1 The Analytically Tractable System	3	(2)
1.1.2 The Analytically Tedious System	5	(2)
1.1.3 The Analytically Intractable System	7	(1)
1.2 Multidisciplinary Aspects of Simulation	8	(3)
1.3 Models	11	(3)
1.4 Deterministic and Stochastic Simulations	14	(5)
1.4.1 An Example of a Deterministic Simulation	16	(1)
1.4.2 An Example of a Stochastic Simulation	17	(2)
1.5 The Role of Simulation	19	(4)
1.5.1 Link Budget and System-Level Specification Process	20	(2)
1.5.2 Implementation and Testing of Key Components	22	(1)
1.5.3 Completion of the Hardware Prototype and Validation of the Simulation Model	22	(1)
1.5.4 End-of-Life Predictions	22	(1)
1.6 Software Packages for Simulation	23	(3)
1.7 A Word of Warning	26	(1)
1.8 The Use of MATLAB	27	(1)
1.9 Outline of the Book	27	(1)
1.10 Further Reading	28	(3)
2 SIMULATION METHODOLOGY	31	(24)
2.1 Introduction	32	(2)
2.2 Aspects of Methodology	34	(15)
2.2.1 Mapping a Problem into a Simulation Model	34	(7)
2.2.2 Modeling of Individual Blocks	41	(6)
2.2.3 Random Process Modeling and simulation	47	(2)
2.3 Performance Estimation	49	(3)
2.4 Summary	52	(1)
2.5 Further Reading	52	(1)
2.6 Problems	52	(3)
Part II Fundamental Concepts and Techniques	55	(392)
3 SAMPLING AND QUANTIZING	55	(40)
3.1 Sampling	56	(9)
3.1.1 The Lowpass Sampling Theorem	56	(5)
3.1.2 Sampling Lowpass Random Signals	61	(1)
3.1.3 Bandpass Sampling	61	(4)
3.2 Quantizing	65	(6)
3.3 Reconstruction and Interpolation	71	(7)
3.3.1 Ideal Reconstruction	71	(1)
3.3.2 Upsampling and Downsampling	72	(6)
3.4 The Simulation Sampling Frequency	78	(9)
3.4.1 General Development	79	(2)
3.4.2 Independent Data Symbols	81	(2)
3.4.3 Simulation Sampling Frequency	83	(4)
3.5 Summary	87	(2)
3.6 Further Reading	89	(1)
3.7 References	90	(1)
3.8 Problems	90	(5)
4 LOWPASS SIMULATION MODELS FOR BANDPASS SIGNALS AND SYSTEMS	95	(48)
4.1 The Lowpass Complex Envelope for Bandpass Signals	95	(23)
4.1.1 The Complex Envelope: The Time-Domain View	96	(12)
4.1.2 The Complex Envelope: The Frequency-Domain View	108	(2)
4.1.3 Derivation of Xd (f) and X9 (f) from X (f)	110	(1)
4.1.4 Energy and Power	111	(1)
4.1.5 Quadrature Models for Random Bandpass signals	112	(3)
4.1.6 Signal-to-Noise Ratios	115	(3)
4.2 Linear Bandpass systems	118	(7)
4.2.1 Linear Time-Invariant Systems	118	(4)
4.2.2 Derivation of hd(t) and hq(t) from H(f)	122	(3)
4.3 Multicarrier Signals	125	(3)
4.4 Nonlinear and Time-Varying Systems	128	(4)
4.4.1 Nonlinear Systems	128	(2)
4.4.2 Time-Varying Systems	130	(2)
4.5 Summary	132	(1)
4.6 Further Reading	133	(1)
4.7 References	134	(1)
4.8 Problems	134	(5)
4.9 Appendix A: MATLAB Program QAMDEMO	139	(2)
4.9.1 Main Program: c4_gamdemo.m	139	(1)
4.9.2 Supporting Routines	140	(1)
4.10 Appendix B: Proof of Input-Output Relationship	141	(2)
5 FILTER MODELS AND SIMULATION TECHNIQUES	143	(58)
5.1 Introduction	144	(2)
5.2 IIR and FIR Filters	146	(2)
5.2.1 IIR Filters	146	(1)
5.2.2 FIR Filters	147	(1)
5.2.3 Synthesis and Simulation	147	(1)
5.3 IIR and FIR Filter Implementations	148	(7)
5.3.1 Direct Form II and Transposed Direct Form II Implementations	148	(6)
5.3.2 FIR Filter Implementation	154	(1)
5.4 IIR Filters: Synthesis Techniques and Filter Characteristics	155	(12)
5.4.1 Impulse-Invariant Filters	155	(1)
5.4.2 Step-Invariant Filters	156	(1)
5.4.3 Bilinear z-Transform Filters	157	(8)
5.4.4 Computer-Aided Design of IIR Digital Filters	165	(2)
5.4.5 Error Sources in IIR Filters	167	(1)
5.5 FIR Filters: Synthesis Techniques and Filter Characteristics	167	(19)
5.5.1 Design from the Amplitude Response	170	(7)
5.5.2 Design from the Impulse Response	177	(3)
5.5.3 Implementation of FIR Filter Simulation Models	180	(4)
5.5.4 Computer-Aided Design of FIR Digital Filters	184	(2)
5.5.5 Comments on FIR Design	186	(1)
5.6 Summary	186	(3)
5.7 Further Reading	189	(1)
5.8 References	189	(1)
5.9 Problems	190	(2)
5.10 Appendix A: Raised Cosine Pulse Example	192	(1)
5.10.1 Main program c5_rcosdemo.m	192	(1)
5.10.2 Function file c5_rcos.m	192	(1)
5.11 Appendix B: Square Root Raised Cosine Pulse Example	193	(1)
5.11.1 Main Program c5_sgrcdemo.m	193	(1)
5.11.2 Function file c5_sgrc.m	193	(1)
5.12 Appendix C: MATLAB Code and Data for Example 5.11	194	(7)
5.12.1 c5_FIRFilterExample.m	195	(1)
5.12.2 FIR_Filter_AMP_Delay.m	196	(2)
5.12.3 shift_ifft.m	198	(1)
5.12.4 log_psd.m	198	(3)
6 CASE STUDY: PHASE-LOCKED LOOPS AND DIFFERENTIAL EQUATION METHODS	201	(42)
6.1 Basic Phase-Locked Loop Concepts	202	(8)
6.1.1 PLL Models	204	(2)
6.1.2 The Nonlinear Phase Model	206	(2)
6.1.3 Nonlinear Model with Complex Input	208	(1)
6.1.4 The Linear Model and the Loop Transfer Function	208	(2)
6.2 First-Order and Second-Order Loops	210	(5)
6.2.1 The First-Order PLL	210	(4)
6.2.2 The Second-Order PLL	214	(1)
6.3 Case Study: Simulating the PLL	215	(8)
6.3.1 The Simulation Architecture	215	(1)
6.3.2 The Simulation	216	(3)
6.3.3 Simulation Results	219	(1)
6.3.4 Error Sources in the Simulation	220	(3)
6.4 Solving Differential Equations Using simulation	223	(7)
6.4.1 Simulation Diagrams	224	(1)
6.4.2 The PLL Revisited	225	(5)
6.5 Summary	230	(1)
6.6 Further Reading	231	(1)
6.7 References	231	(1)
6.8 Problems	232	(4)
6.9 Appendix A: PLL Simulation Program	236	(1)
6.10 Appendix B: Preprocessor for PLL Example Simulation	237	(1)
6.11 Appendix C: PLL Postprocessor	238	(3)
6.11.1 Main Program	238	(1)
6.11.2 Called Routines	239	(2)
6.12 Appendix D: MATLAB Code for Example 6.3	241	(2)
7 GENERATING AND PROCESSING RANDOM SIGNALS	243	(60)
7.1 Stationary and Ergodic Processes	244	(4)
7.2 Uniform Random Number Generators	248	(10)
7.2.1 Linear Congruence	248	(4)
7.2.2 Testing Random Number Generators	252	(4)
7.2.3 Minimum Standards	256	(1)
7.2.4 MATLAB Implementation	257	(1)
7.2.5 Seed Numbers and Vectors	258	(1)
7.3 Mapping Uniform RVs to an Arbitrary pdf	258	(11)
7.3.1 The Inverse Transform Method	259	(5)
7.3.2 The Histogram Method	264	(2)
7.3.3 Rejection Methods	266	(3)
7.4 Generating Uncorrelated Gaussian Random Numbers	269	(8)
7.4.1 The Sum of Uniforms Method	270	(3)
7.4.2 Mapping a Rayleigh RV to a Gaussian RV	273	(2)
7.4.3 The Polar Method	275	(1)
7.4.4 MATLAB Implementation	276	(1)
7.5 Generating Correlated Gaussian Random Numbers	277	(5)
7.5.1 Establishing a Given Correlation Coefficient	277	(1)
7.5.2 Establishing an Arbitrary PSD or Autocorrelation Function	278	(4)
7.6 Establishing a pdf and a PSD	282	(1)
7.7 PN Sequence Generators	283	(7)
7.8 Signal Processing	290	(3)
7.8.1 Input/Output Means	291	(1)
7.8.2 Input/Output Cross-Correlation	291	(1)
7.8.3 Output Autocorrelation Function	292	(1)
7.8.4 Input/Output Variances	293	(1)
7.9 Summary	293	(1)
7.10 Further Reading	294	(1)
7.11 References	294	(1)
7.12 Problems	295	(4)
7.13 Appendix A: MATLAB Code for Example 7.11	299	(1)
7.14 Main Program: c7_Jakes.m	299	(4)
7.14.1 Supporting Routines	300	(3)
8 POSTPROCESSING	303	(44)
8.1 Basic Graphical Techniques	304	(5)
8.1.1 A System Example-π/4 DQPSK Transmission	304	(3)
8.1.2 Waveforms, Eye Diagrams, and Scatter Plots	307	(2)
8.2 Estimation	309	(20)
8.2.1 Histograms	309	(7)
8.2.2 Power Spectral Density Estimation	316	(7)
8.2.3 Gain, Delay, and Signal-to-Noise Ratios	323	(6)
8.3 Coding	329	(7)
8.3.1 Analytic Approach to Block Coding	330	(3)
8.3.2 Analytic Approach to Convolutional Coding	333	(3)
8.4 Summary	336	(1)
8.5 Further Reading	336	(2)
8.6 References	338	(1)
8.7 Problems	339	(3)
8.8 Appendix A: MATLAB Code for Example 8.1	342	(5)
8.8.1 Main Program: c8_pi4demo.m	342	(2)
8.8.2 Supporting Routines	344	(3)
9 INTRODUCTION TO MONTE CARLO METHODS	347	(32)
9.1 Fundamental Concepts	347	(7)
9.1.1 Relative Frequency	348	(1)
9.1.2 Unbiased and Consistent Estimators	349	(1)
9.1.3 Monte Carlo Estimation	349	(2)
9.1.4 The Estimation of π	351	(3)
9.2 Application to Communications Systems The AWGN Channel	354	(12)
9.2.1 The Binomial Distribution	355	(4)
9.2.2 Two Simple Monte Carlo Simulations	359	(7)
9.3 Monte Carlo Integration	366	(9)
9.3.1 Basic Concepts	368	(2)
9.3.2 Convergence	370	(1)
9.3.3 Confidence Intervals	371	(4)
9.4 Summary	375	(1)
9.5 Further Reading	375	(1)
9.6 References	375	(1)
9.7 Problems	376	(3)
10 MONTE CARLO SIMULATION OF COMMUNICATION SYSTEMS	379	(42)
10.1 Two Monte Carlo Examples	380	(13)
10.2 Semianalytic Techniques	393	(12)
10.2.1 Basic Considerations	394	(3)
10.2.2 Equivalent Noise sources	397	(1)
10.2.3 Semianalytic BER Estimation for PSK	398	(2)
10.2.4 Semianalytic BER Estimation for QPSK	400	(4)
10.2.5 Choice of Data Sequence	404	(1)
10.3 Summary	405	(1)
10.4 References	406	(1)
10.5 Problems	406	(2)
10.6 Appendix A: Simulation Code for Example 10.1	408	(2)
10.6.1 Main Program	408	(1)
10.6.2 Supporting Program: random_binary.m	409	(1)
10.7 Appendix B: Simulation Code for Example 10.2	410	(5)
10.7.1 Main Program	410	(4)
10.7.2 Supporting Programs	414	(1)
10.7.3 vxcorr.m	414	(1)
10.8 Appendix C: Simulation Code for Example 10.3	415	(3)
10.8.1 Main Program: c10_SKSA.m	415	(1)
10.8.2 Supporting Programs	416	(2)
10.9 Appendix D: Simulation Code for Example 10.4	418	(3)
10.9.1 Supporting Programs	419	(2)
11 METHODOLOGY FOR SIMULATING A WIRELESS SYSTEM	421	(26)
11.1 System-Level Simplifications and Sampling Rate Considerations	423	(1)
11.2 Overall Methodology	424	(19)
11.2.1 Methodology for Simulation of the Analog Portion of the System	429	(12)
11.2.2 Summary of Methodology for Simulating the Analog Portion of the System	441	(1)
11.2.3 Estimation of the Coded BER	441	(1)
11.2.4 Estimation of Voice-Quality Metric	441	(1)
11.2.5 Summary of Overall Methodology	442	(1)
11.3 Summary	443	(1)
11.4 Further Reading	443	(1)
11.5 References	444	(1)
11.6 Problems	444	(3)
Part III Advanced Models and Simulation Techniques	447	(320)
12 MODELING AND SIMULATION OF NONLINEARITIES	447	(50)
12.1 Introduction	448	(3)
12.1.1 Types of Nonlinearities and Models	448	(1)
12.1.2 Simulation of Nonlinearities-Factors to Consider	449	(2)
12.2 Modeling and Simulation of Memoryless Nonlinearities	451	(17)
12.2.1 Baseband Nonlinearities	452	(1)
12.2.2 Bandpass Nonlinearities-Zonal Bandpass Model	453	(2)
12.2.3 Lowpass Complex Envelope (AM-to-AM and AM-to-PM) Models	455	(6)
12.2.4 Simulation of Complex Envelope Models	461	(1)
12.2.5 The Multicarrier Case	462	(6)
12.3 Modeling and Simulation of Nonlinearities with Memory	468	(7)
12.3.1 Empirical Models Based on swept Tone Measurements	470	(2)
12.3.2 Other Models	472	(3)
12.4 Techniques for Solving Nonlinear Differential Equations	475	(11)
12.4.1 State Vector Form of the NLDE	476	(3)
12.4.2 Recursive Solutions of NLDE-Scalar Case	479	(4)
12.4.3 General Form of Multistep Methods	483	(1)
12.4.4 Accuracy and Stability of Numerical Integration Methods	483	(2)
12.4.5 Solution of Higher-Order NLDE-Vector Case	485	(1)
12.5 PLL Example	486	(2)
12.5.1 Integration Methods	486	(2)
12.6 Summary	488	(1)
12.7 Further Reading	488	(1)
12.8 References	489	(1)
12.9 Problems	490	(3)
12.10 Appendix A: Saleh's Model	493	(1)
12.11 Appendix B: MATLAB Code for Example 12.2	494	(3)
12.11.1 Supporting Routines	495	(2)
13 MODELING AND SIMULATION OF TIME-VARYING SYSTEMS	497	(32)
13.1 Introduction	497	(3)
13.1.1 Examples of Time-Varying Systems	498	(1)
13.1.2 Modeling and Simulation Approach	499	(1)
13.2 Models for LTV Systems	500	(11)
13.2.1 Time-Domain Description for LTV System	500	(3)
13.2.2 Frequency Domain Description of LTV Systems	503	(2)
13.2.3 Properties of LTV Systems	505	(6)
13.3 Random Process Models	511	(4)
13.4 Simulation Models for LTV Systems	515	(3)
13.4.1 Tapped Delay Line Model	515	(3)
13.5 MATLAB Examples	518	(4)
13.5.1 MATLAB Example 1	518	(2)
13.5.2 MATLAB Example 2	520	(2)
13.6 Summary	522	(1)
13.7 Further Reading	523	(1)
13.8 References	523	(1)
13.9 Problems	523	(2)
13.10 Appendix A: Code for MATLAB Example 1	525	(2)
13.10.1 Supporting Program	526	(1)
13.11 Appendix B: Code for MATLAB Example 2	527	(2)
13.11.1 Supporting Routines	528	(1)
13.11.2 mpsk_pulses.m	528	(1)
14 MODELING AND SIMULATION OF WAVEFORM CHANNELS	529	(54)
14.1 Introduction	529	(4)
14.1.1 Models of Communication Channels	530	(1)
14.1.2 Simulation of Communication Channels	531	(1)
14.1.3 Discrete Channel Models	532	(1)
14.1.4 Methodology for Simulating Communication System Performance	532	(1)
14.1.5 Outline of Chapter	533	(1)
14.2 Wired and Guided Wave Channels	533	(1)
14.3 Radio Channels	534	(4)
14.3.1 Tropospheric Channel	536	(1)
14.3.2 Rain Effects on Radio Channels	537	(1)
14.4 Multipath Fading Channels	538	(8)
14.4.1 Introduction	538	(1)
14.4.2 Example of a Multipath Fading Channel	538	(7)
14.4.3 Discrete Versus Diffused Multipath	545	(1)
14.5 Modeling Multipath Fading Channels	546	(1)
14.6 Random Process Models	547	(5)
14.6.1 Models for Temporal Variations in the Channel Response (Fading)	549	(1)
14.6.2 Important Parameters	550	(2)
14.7 Simulation Methodology	552	(19)
14.7.1 Simulation of Diffused Multipath Fading Channels	553	(5)
14.7.2 Simulation of Discrete Multipath Fading Channels	558	(7)
14.7.3 Examples of Discrete Multipath Fading Channel Models	565	(6)
14.7.4 Models for Indoor Wireless Channels	571	(1)
14.8 Summary	571	(1)
14.9 Further Reading	572	(1)
14.10 References	572	(3)
14.11 Problems	575	(2)
14.12 Appendix A: MATLAB Code for Example 14.1	577	(3)
14.12.1 Main Program	577	(1)
14.12.2 Supporting Functions	578	(2)
14.13 Appendix B: MATLAB Code for Example 14.2	580	(3)
14.13.1 Main Program	580	(1)
14.13.2 Supporting Functions	581	(2)
15 DISCRETE CHANNEL MODELS	583	(56)
15.1 Introduction	584	(2)
15.2 Discrete Memoryless Channel Models	586	(3)
15.3 Markov Models for Discrete Channels with Memory	589	(12)
15.3.1 Two-State Model	589	(7)
15.3.2 N-state Markov Model	596	(1)
15.3.3 First-Order Markov Process	597	(1)
15.3.4 Stationarity	597	(1)
15.3.5 Simulation of the Markov Model	598	(3)
15.4 Example HMMs-Gilbert and Fritchman Models	601	(3)
15.5 Estimation of Markov Model Parameters	604	(11)
15.5.1 Scaling	611	(1)
15.5.2 Convergence and Stopping Criteria	612	(1)
15.5.3 Block Equivalent Markov Models	613	(2)
15.6 Two Examples	615	(6)
15.7 Summary	621	(1)
15.8 Further Reading	622	(1)
15.9 References	622	(1)
15.10 Problems	623	(4)
15.11 Appendix A: Error Vector Generation	627	(2)
15.11.1 Program: c15_errvector.m	627	(1)
15.11.2 Program: c15 hmmtest.m	628	(1)
15.12 Appendix B: The Baum-Welch Algorithm	629	(3)
15.13 Appendix C: The Semi-Hidden Markov Model	632	(4)
15.14 Appendix D: Run-Length Code Generation	636	(1)
15.15 Appendix E: Determination of Error-Free Distribution	637	(2)
15.15.1 c15_intervals1.m	637	(1)
15.15.2 c15_intervals2.m	637	(2)
16 EFFICIENT SIMULATION TECHNIQUES	639	(32)
16.1 Tail Extrapolation	640	(2)
16.2 pdf Estimators	642	(3)
16.3 Importance sampling	645	(15)
16.3.1 Area of an Ellipse	646	(9)
16.3.2 Sensitivity to the pdf	655	(1)
16.3.3 A Final Twist	656	(1)
16.3.4 The Communication Problem	657	(2)
16.3.5 Conventional and Improved Importance Sampling	659	(1)
16.4 Summary	660	(1)
16.5 Further Reading	660	(2)
16.6 References	662	(1)
16.7 Problems	662	(3)
16.8 Appendix A: MATLAB Code for Example 16.3	665	(6)
16.8.1 Supporting Routines	669	(2)
17 CASE STUDY: SIMULATION OF A CELLULAR RADIO SYSTEM	671	(48)
17.1 Introduction	671	(2)
17.2 Cellular Radio System	673	(15)
17.2.1 System-Level Description	673	(3)
17.2.2 Modeling a Cellular Communication System	676	(12)
17.3 Simulation Methodology	688	(18)
17.3.1 The Simulation	688	(12)
17.3.2 Processing the Simulation Results	700	(6)
17.4 Summary	706	(1)
17.5 Further Reading	706	(1)
17.6 References	707	(1)
17.7 Problems	708	(2)
17.8 Appendix A: Program for Generating the Erlang B Chart	710	(2)
17.9 Appendix B: Initialization Code for Simulation	712	(2)
17.10 Appendix C: Modeling Co-Channel Interference	714	(4)
17.10.1 Wilkinson's Method	715	(2)
17.10.2 Schwartz and Yeh's Method	717	(1)
17.11 Appendix D: MATLAB Code for Wilkinson's Method	718	(1)
18 TWO EXAMPLE SIMULATIONS	719	(48)
18.1 A Code-Division Multiple Access system	720	(14)
18.1.1 The System	720	(4)
18.1.2 The Simulation Program	724	(2)
18.1.3 Example Simulations	726	(3)
18.1.4 Development of Markov Models	729	(5)
18.2 FDM System with a Nonlinear Satellite Transponder	734	(12)
18.2.1 System Description and Simulation Objectives	734	(3)
18.2.2 The Overall Simulation Model	737	(1)
18.2.3 Uplink FDM Signal Generation	738	(2)
18.2.4 Satellite Transponder Model	740	(1)
18.2.5 Receiver Model and Semianalytic BER Estimator	741	(1)
18.2.6 Simulation Results	742	(2)
18.2.7 Summary and Conclusions	744	(2)
18.3 References	746	(1)
18.4 Appendix A: MATLAB Code for CDMA Example	747	(6)
18.4.1 Supporting Functions	750	(3)
18.5 Appendix B: Preprocessors for CDMA Application	753	(2)
18.5.1 Validation Run	753	(1)
18.5.2 Study Illustrating the Effect of the Ricean K-Factor	753	(2)
18.6 Appendix C: MATLAB Function c18_errvector.m	755	(1)
18.7 Appendix D: MATLAB Code for Satellite FDM Example	756	(11)
18.7.1 Supporting Functions	760	(7)
INDEX	767	(8)
ABOUT THE AUTHORS	775

Excerpts

Preface This book is a result of the recent rapid advances in two related technologies: communications and computers. Over the past few decades, communication systems have increased in complexity to the point where system design and performance analysis can no longer be conducted without a significant level of computer support. Many of the communication systems of fifty years ago were either power or noise limited. A significant degrading effect in many of these systems was thermal noise, which was modeled using the additive Gaussian noise channel. Many modern communication systems, however, such as the wireless cellular system, operate in environments that are interference and bandwidth limited. In addition, the desire for wideband channels and miniature components pushes transmission frequencies into the gigahertz range, where propagation characteristics are more complicated and multipath-induced fading is a common problem. In order to combat these effects, complex receiver structures, such as those using complicated synchronization structures, demodulators and symbol estimators, and RAKE processors, are often used. Many of these systems are not analytically tractable using non-computer based techniques, and simulation is often necessary for the design and analysis of these systems. The same advances in technology that made modern communication systems possible, namely microprocessors and DSP techniques, also provided us with highspeed digital computers. The modern workstation and personal computer (PC) have computational capabilities greatly exceeding the mainframe computers used just a few years ago. In addition, modern workstations and PCs are inexpensive and therefore available at the desktop of design engineers. As a result, simulationbased design and analysis techniques are practical tools widely used throughout the communications industry. As a result, graduate-level courses dealing with simulation-based design and analysis of communication systems are becoming more common. Students derive a number of benefits from these courses. Through the use of simulation, students in communications courses can study the operating characteristics of systems that are more complex and more real world than those studied in traditional communications courses since, in traditional courses, complexity must be constrained to ensure that analyses can be conducted. Simulation allows system parameters to be easily changed, and the impact of these changes can be rapidly evaluated by using interactive and visual displays of simulation results. In addition, an understanding of simulation techniques supports the research programs of many graduate students working in the communications area. Finally, students going into the communications industry upon graduation have skills needed by industry. This book is intended to support these courses. A number of the applications and examples discussed in this book are targeted to wireless communication systems. This was done for several reasons. First, many students studying communications will eventually work in the wireless industry. Also, a significant number of graduate students pursuing university-based research are working on problems related to wireless communications. Finally, as a result of the high level of interest in wireless communications, many graduate programs contain courses in wireless communications. This book is designed to support, at least in part, these courses, as well as the self-study needs of the working engineer. This book is divided into three major sections. The first section, Introduction, consists of two chapters. The first of these introductory chapters discusses the motivation for using simulation in both the analysis and the design process. The theory of simulation is shown to draw on several classic fields of study such as number theory, probability theory, stochastic processes, and digital signal processing, to name only a few.