Principles of Communication Systems Simulation with Wireless Applications

Principles of Communication Systems Simulation with Wireless Applications

by K. Sam Shanmugan, Theodore S. Rappaport, William H. Tranter, Kurt L. Kosbar
Released December 2003
Publisher(s): Pearson
ISBN: 0134947908

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Book description

The hands-on, example-rich guide to modeling and simulating advanced communications systems.

Simulation is an important tool used by engineers to design and implement advanced communication systems that deliver optimal performance. This book is a hands-on, example-rich guide to modeling and simulating advanced communications systems. The authors take a systems-level approach, integrating digital communications, channel modeling, coding, elementary statistical estimation techniques, and other essential facets of modeling and simulation. This is the first book to present complete simulation models built with MATLAB that can serve as virtual laboratories for predicting the impact of system design changes. Coverage includes:

  • Role of simulation in communication systems engineering

  • Simulation approaches and methodologies

  • Signal and system representations, filter models, noise generation, Monte Carlo simulation, and postprocessing

  • Advanced techniques for modeling and simulating nonlinear and time-varying systems

  • Waveform level and discrete channel models

  • Performance estimation via Monte Carlo simulation

  • Semianalytic simulation techniques

  • Variance reduction techniques

  • Co-channel interference in wireless communication systems, and more

    • The authors also present detailed case studies covering phase-locked loops, CDMA systems, multichannel nonlinear systems, as well as a start-to-finish simulation of an advanced cellular radio system.

    Prentice Hall Series in Communications Engineering & Emerging Technologies, Theodore S. Rappaport, Editor

    Table of contents

    1. Copyright
      1. Dedication
    2. Prentice Hall Communications Engineering and Emerging Technologies Series
    3. Preface
      1. Acknowledgments
    4. I. Introduction
      1. 1. The Role of Simulation
        1. 1.1. Examples of Complexity
          1. 1.1.1. The Analytically Tractable System
          2. 1.1.2. The Analytically Tedious System
          3. 1.1.3. The Analytically Intractable System
        2. 1.2. Multidisciplinary Aspects of Simulation
        3. 1.3. Models
        4. 1.4. Deterministic and Stochastic Simulations
          1. 1.4.1. An Example of a Deterministic Simulation
          2. 1.4.2. An Example of a Stochastic Simulation
        5. 1.5. The Role of Simulation
          1. 1.5.1. Link Budget and System-Level Specification Process
          2. 1.5.2. Implementation and Testing of Key Components
          3. 1.5.3. Completion of the Hardware Prototype and Validation of the Simulation Model
          4. 1.5.4. End-of-Life Predictions
        6. 1.6. Software Packages for Simulation
        7. 1.7. A Word of Warning
        8. 1.8. The Use of MATLAB
        9. 1.9. Outline of the Book
        10. 1.10. Further Reading
      2. 2. Simulation Methodology
        1. 2.1. Introduction
        2. 2.2. Aspects of Methodology
          1. 2.2.1. Mapping a Problem into a Simulation Model
            1. Hierarchical Representation
            2. Partitioning and Conditioning
            3. Simplifications and Approximations
          2. 2.2.2. Modeling of Individual Blocks
            1. Lowpass Equivalent Representation
            2. Sampling
            3. Linear versus Nonlinear Models
            4. Time Invariance
            5. Memory
            6. Time-Domain and Frequency-Domain Simulations
            7. Block Processing
            8. Variable Step-Size Processing
            9. Parameterization
            10. Interface to Other Blocks
          3. 2.2.3. Random Process Modeling and Simulation
            1. Gaussian Approximation
            2. Equivalent Process Representation
            3. Slow versus Fast Processes
        3. 2.3. Performance Estimation
        4. 2.4. Summary
        5. 2.5. Further Reading
        6. 2.6. Problems
    5. II. Fundamental Concepts and Techniques
      1. 3. Sampling and Quantizing
        1. 3.1. Sampling
          1. 3.1.1. The Lowpass Sampling Theorem
          2. 3.1.2. Sampling Lowpass Random Signals
          3. 3.1.3. Bandpass Sampling
            1. The Bandpass Sampling Theorem
            2. Sampling Direct/Quadrature Signals
        2. 3.2. Quantizing
          1. Fixed-Point Arithmetic
          2. Floating-Point Arithmetic
        3. 3.3. Reconstruction and Interpolation
          1. 3.3.1. Ideal Reconstruction
          2. 3.3.2. Upsampling and Downsampling
            1. Upsampling and Interpolation
            2. Downsampling (Decimation)
        4. 3.4. The Simulation Sampling Frequency
          1. 3.4.1. General Development
          2. 3.4.2. Independent Data Symbols
          3. 3.4.3. Simulation Sampling Frequency
        5. 3.5. Summary
        6. 3.6. Further Reading
        7. 3.7. References
        8. 3.8. Problems
      2. 4. Lowpass Simulation Models for Bandpass Signals and Systems
        1. 4.1. The Lowpass Complex Envelope for Bandpass Signals
          1. 4.1.1. The Complex Envelope: The Time-Domain View
          2. 4.1.2. The Complex Envelope: The Frequency-Domain View
          3. 4.1.3. Derivation of Xd(f) and Xq(f) from
          4. 4.1.4. Energy and Power
          5. 4.1.5. Quadrature Models for Random Bandpass Signals
          6. 4.1.6. Signal-to-Noise Ratios
        2. 4.2. Linear Bandpass Systems
          1. 4.2.1. Linear Time-Invariant Systems
          2. 4.2.2. Derivation of hd(t) and hq(t) from H(f)
        3. 4.3. Multicarrier Signals
        4. 4.4. Nonlinear and Time-Varying Systems
          1. 4.4.1. Nonlinear Systems
          2. 4.4.2. Time-Varying Systems
        5. 4.5. Summary
        6. 4.6. Further Reading
        7. 4.7. References
        8. 4.8. Problems
        9. 4.9. Appendix A: MATLAB Program QAMDEMO
          1. 4.9.1. Main Program: c4_qamdemo.m
          2. 4.9.2. Supporting Routines
            1. qam.m
            2. mary.m
        10. 4.10. Appendix B: Proof of Input-Output Relationship
      3. 5. Filter Models and Simulation Techniques
        1. 5.1. Introduction
        2. 5.2. IIR and FIR Filters
          1. 5.2.1. IIR Filters
          2. 5.2.2. FIR Filters
          3. 5.2.3. Synthesis and Simulation
        3. 5.3. IIR and FIR Filter Implementations
          1. 5.3.1. Direct Form II and Transposed Direct Form II Implementations
          2. 5.3.2. FIR Filter Implementation
        4. 5.4. IIR Filters: Synthesis Techniques and Filter Characteristics
          1. 5.4.1. Impulse-Invariant Filters
          2. 5.4.2. Step-Invariant Filters
          3. 5.4.3. Bilinear z-Transform Filters
            1. Synthesis Technique
            2. Special Case: Trapezoidal Integration
          4. 5.4.4. Computer-Aided Design of IIR Digital Filters
          5. 5.4.5. Error Sources in IIR Filters
        5. 5.5. FIR Filters: Synthesis Techniques and Filter Characteristics
          1. 5.5.1. Design from the Amplitude Response
          2. 5.5.2. Design from the Impulse Response
          3. 5.5.3. Implementation of FIR Filter Simulation Models
          4. 5.5.4. Computer-Aided Design of FIR Digital Filters
          5. 5.5.5. Comments on FIR Design
        6. 5.6. Summary
        7. 5.7. Further Reading
        8. 5.8. References
        9. 5.9. Problems
        10. 5.10. Appendix A: Raised Cosine Pulse Example
          1. 5.10.1. Main program c5_rcosdemo.m
          2. 5.10.2. Function file c5_rcos.m
        11. 5.11. Appendix B: Square Root Raised Cosine Pulse Example
          1. 5.11.1. Main Program c5_sqrcdemo.m
          2. 5.11.2. Function file c5_sqrc.m
        12. 5.12. Appendix C: MATLAB Code and Data for Example 5.11
          1. 5.12.1. c5_FIRFilterExample.m
          2. 5.12.2. FIR_Filter_AMP_Delay.m
          3. 5.12.3. shift_ifft.m
          4. 5.12.4. log_psd.m
      4. 6. Case Study: Phase-Locked Loops and Differential Equation Methods
        1. 6.1. Basic Phase-Locked Loop Concepts
          1. 6.1.1. PLL Models
          2. 6.1.2. The Nonlinear Phase Model
          3. 6.1.3. Nonlinear Model with Complex Input
          4. 6.1.4. The Linear Model and the Loop Transfer Function
        2. 6.2. First-Order and Second-Order Loops
          1. 6.2.1. The First-Order PLL
          2. 6.2.2. The Second-Order PLL
        3. 6.3. Case Study: Simulating the PLL
          1. 6.3.1. The Simulation Architecture
          2. 6.3.2. The Simulation
          3. 6.3.3. Simulation Results
          4. 6.3.4. Error Sources in the Simulation
            1. The Analytical Model
            2. The Simulation Model
        4. 6.4. Solving Differential Equations Using Simulation
          1. 6.4.1. Simulation Diagrams
          2. 6.4.2. The PLL Revisited
        5. 6.5. Summary
        6. 6.6. Further Reading
        7. 6.7. References
        8. 6.8. Problems
        9. 6.9. Appendix A: PLL Simulation Program
        10. 6.10. Appendix B: Preprocessor for PLL Example Simulation
        11. 6.11. Appendix C: PLL Postprocessor
          1. 6.11.1. Main Program
          2. 6.11.2. Called Routines
            1. Script File ppplot.m
            2. Function pplane.m
        12. 6.12. Appendix D: MATLAB Code for Example 6.3
      5. 7. Generating and Processing Random Signals
        1. 7.1. Stationary and Ergodic Processes
        2. 7.2. Uniform Random Number Generators
          1. 7.2.1. Linear Congruence
            1. Technique A: The Mixed Congruence Algorithm
            2. Technique B: The Multiplicative Algorithm With Prime Modulus
            3. Technique C: The Multiplicative Algorithm with Nonprime Modulus
          2. 7.2.2. Testing Random Number Generators
            1. Scatterplots
            2. The Durbin-Watson Test
          3. 7.2.3. Minimum Standards
            1. Lewis, Goodman, and Miller Minimum Standard
            2. The Wichmann-Hill Algorithm
          4. 7.2.4. MATLAB Implementation
          5. 7.2.5. Seed Numbers and Vectors
        3. 7.3. Mapping Uniform RVs to an Arbitrary pdf
          1. 7.3.1. The Inverse Transform Method
          2. 7.3.2. The Histogram Method
          3. 7.3.3. Rejection Methods
        4. 7.4. Generating Uncorrelated Gaussian Random Numbers
          1. 7.4.1. The Sum of Uniforms Method
          2. 7.4.2. Mapping a Rayleigh RV to a Gaussian RV
          3. 7.4.3. The Polar Method
          4. 7.4.4. MATLAB Implementation
        5. 7.5. Generating Correlated Gaussian Random Numbers
          1. 7.5.1. Establishing a Given Correlation Coefficient
          2. 7.5.2. Establishing an Arbitrary PSD or Autocorrelation Function
        6. 7.6. Establishing a pdf and a PSD
        7. 7.7. PN Sequence Generators
        8. 7.8. Signal Processing
          1. 7.8.1. Input/Output Means
          2. 7.8.2. Input/Output Cross-Correlation
          3. 7.8.3. Output Autocorrelation Function
          4. 7.8.4. Input/Output Variances
        9. 7.9. Summary
        10. 7.10. Further Reading
        11. 7.11. References
        12. 7.12. Problems
        13. 7.13. Appendix A: MATLAB Code for Example 7.11
        14. 7.14. Main Program: c7_Jakes.m
          1. 7.14.1. Supporting Routines
            1. Jakes_filter.m
            2. linear_fft.m
            3. log_psd.m
      6. 8. Postprocessing
        1. 8.1. Basic Graphical Techniques
          1. 8.1.1. A System Example—π/4 DQPSK Transmission
          2. 8.1.2. Waveforms, Eye Diagrams, and Scatter Plots
            1. Eye Diagrams
        2. 8.2. Estimation
          1. 8.2.1. Histograms
          2. 8.2.2. Power Spectral Density Estimation
            1. The Periodogram
            2. The Periodogram With a Data Window
            3. Segmented Periodograms
          3. 8.2.3. Gain, Delay, and Signal-to-Noise Ratios
            1. Theoretical Development for Real Lowpass Signals
        3. 8.3. Coding
          1. 8.3.1. Analytic Approach to Block Coding
          2. 8.3.2. Analytic Approach to Convolutional Coding
        4. 8.4. Summary
        5. 8.5. Further Reading
        6. 8.6. References
        7. 8.7. Problems
        8. 8.8. Appendix A: MATLAB Code for Example 8.1
          1. 8.8.1. Main Program: c8_pi4demo.m
          2. 8.8.2. Supporting Routines
            1. sigcon.m
            2. dqeye.m
            3. dqplot.m
      7. 9. Introduction to Monte Carlo Methods
        1. 9.1. Fundamental Concepts
          1. 9.1.1. Relative Frequency
          2. 9.1.2. Unbiased and Consistent Estimators
          3. 9.1.3. Monte Carlo Estimation
          4. 9.1.4. The Estimation of π
        2. 9.2. Application to Communications Systems—The AWGN Channel
          1. 9.2.1. The Binomial Distribution
          2. 9.2.2. Two Simple Monte Carlo Simulations
        3. 9.3. Monte Carlo Integration
          1. 9.3.1. Basic Concepts
          2. 9.3.2. Convergence
          3. 9.3.3. Confidence Intervals
        4. 9.4. Summary
        5. 9.5. Further Reading
        6. 9.6. References
        7. 9.7. Problems
      8. 10. Monte Carlo Simulation of Communication Systems
        1. 10.1. Two Monte Carlo Examples
        2. 10.2. Semianalytic Techniques
          1. 10.2.1. Basic Considerations
          2. 10.2.2. Equivalent Noise Sources
          3. 10.2.3. Semianalytic BER Estimation for PSK
          4. 10.2.4. Semianalytic BER Estimation for QPSK
          5. 10.2.5. Choice of Data Sequence
        3. 10.3. Summary
        4. 10.4. References
        5. 10.5. Problems
        6. 10.6. Appendix A: Simulation Code for Example 10.1
          1. 10.6.1. Main Program
          2. 10.6.2. Supporting Program: random_binary.m
        7. 10.7. Appendix B: Simulation Code for Example 10.2
          1. 10.7.1. Main Program
          2. 10.7.2. Supporting Programs
          3. 10.7.3. vxcorr.m
        8. 10.8. Appendix C: Simulation Code for Example 10.3
          1. 10.8.1. Main Program: c10_PSKSA.m
          2. 10.8.2. Supporting Programs
            1. psk_berest
            2. q.m
        9. 10.9. Appendix D: Simulation Code for Example 10.4
          1. 10.9.1. Supporting Programs
            1. qpsk_berest
      9. 11. Methodology for Simulating a Wireless System
        1. 11.1. System-Level Simplifications and Sampling Rate Considerations
          1. Sampling Rate
        2. 11.2. Overall Methodology
          1. 11.2.1. Methodology for Simulation of the Analog Portion of the System
            1. Details of the Simulation Model
            2. Pure Monte Carlo Approach to Performance Estimation
            3. Semianalytic Approach to Performance Estimation
            4. Faster Semianalytic Technique
            5. Moment Method for BER Estimation
          2. 11.2.2. Summary of Methodology for Simulating the Analog Portion of the System
          3. 11.2.3. Estimation of the Coded BER
          4. 11.2.4. Estimation of Voice-Quality Metric
          5. 11.2.5. Summary of Overall Methodology
        3. 11.3. Summary
        4. 11.4. Further Reading
        5. 11.5. References
        6. 11.6. Problems
    6. III. Advanced Models and Simulation Techniques
      1. 12. Modeling and Simulation of Nonlinearities
        1. 12.1. Introduction
          1. 12.1.1. Types of Nonlinearities and Models
          2. 12.1.2. Simulation of Nonlinearities—Factors to Consider
            1. Sampling Rate
            2. Cascading
            3. Nonlinear Feedback Loops
            4. Variable Sampling Rate and Interpolation
        2. 12.2. Modeling and Simulation of Memoryless Nonlinearities
          1. 12.2.1. Baseband Nonlinearities
          2. 12.2.2. Bandpass Nonlinearities—Zonal Bandpass Model
          3. 12.2.3. Lowpass Complex Envelope (AM-to-AM and AM-to-PM) Models
            1. Analytical Derivation of AM–to-AM and AM-to-PM Characteristics
            2. Measurement of AM-to-AM and AM-to-PM Characteristics
            3. Analytical Forms of AM-to-AM and AM-to-PM Characteristics
          4. 12.2.4. Simulation of Complex Envelope Models
          5. 12.2.5. The Multicarrier Case
            1. The Multicarrier Model
            2. Intermodulation Distortion in Multicarrier Systems
        3. 12.3. Modeling and Simulation of Nonlinearities with Memory
          1. 12.3.1. Empirical Models Based on Swept Tone Measurements
            1. Poza’s Model
            2. Saleh’s Model
          2. 12.3.2. Other Models
        4. 12.4. Techniques for Solving Nonlinear Differential Equations
          1. 12.4.1. State Vector Form of the NLDE
          2. 12.4.2. Recursive Solutions of NLDE-Scalar Case
            1. Explicit Techniques
            2. Implicit Techniques
            3. Implicit Solution Using the Predictor-Corrector Method
            4. Implicit Solution Using Newton-Raphson Method
          3. 12.4.3. General Form of Multistep Methods
          4. 12.4.4. Accuracy and Stability of Numerical Integration Methods
            1. Accuracy
            2. Stability
          5. 12.4.5. Solution of Higher-Order NLDE-Vector Case
        5. 12.5. PLL Example
          1. 12.5.1. Integration Methods
            1. Forward Euler (Explicit Method)
            2. Backward Euler (Implicit Method with Predictor-Corrector)
            3. Backward Euler (Implicit Method with N-R Iterations)
        6. 12.6. Summary
        7. 12.7. Further Reading
        8. 12.8. References
        9. 12.9. Problems
        10. 12.10. Appendix A: Saleh’s Model
        11. 12.11. Appendix B: MATLAB Code for Example 12.2
          1. 12.11.1. Supporting Routines
      2. 13. Modeling and Simulation of Time-Varying Systems
        1. 13.1. Introduction
          1. 13.1.1. Examples of Time-Varying Systems
          2. 13.1.2. Modeling and Simulation Approach
        2. 13.2. Models for LTV Systems
          1. 13.2.1. Time-Domain Description for LTV System
          2. 13.2.2. Frequency Domain Description of LTV Systems
          3. 13.2.3. Properties of LTV Systems
            1. Associative Property
            2. Commutative Property
            3. Distributive Property
        3. 13.3. Random Process Models
        4. 13.4. Simulation Models for LTV Systems
          1. 13.4.1. Tapped Delay Line Model
            1. Simplification of the TDL Model
            2. Generation of Tap Gain Processes
        5. 13.5. MATLAB Examples
          1. 13.5.1. MATLAB Example 1
          2. 13.5.2. MATLAB Example 2
        6. 13.6. Summary
        7. 13.7. Further Reading
        8. 13.8. References
        9. 13.9. Problems
        10. 13.10. Appendix A: Code for MATLAB Example 1
          1. 13.10.1. Supporting Program
        11. 13.11. Appendix B: Code for MATLAB Example 2
          1. 13.11.1. Supporting Routines
          2. 13.11.2. mpsk_pulses.m
      3. 14. Modeling and Simulation of Waveform Channels
        1. 14.1. Introduction
          1. 14.1.1. Models of Communication Channels
          2. 14.1.2. Simulation of Communication Channels
          3. 14.1.3. Discrete Channel Models
          4. 14.1.4. Methodology for Simulating Communication System Performance
          5. 14.1.5. Outline of Chapter
        2. 14.2. Wired and Guided Wave Channels
        3. 14.3. Radio Channels
          1. 14.3.1. Tropospheric Channel
          2. 14.3.2. Rain Effects on Radio Channels
        4. 14.4. Multipath Fading Channels
          1. 14.4.1. Introduction
          2. 14.4.2. Example of a Multipath Fading Channel
          3. 14.4.3. Discrete Versus Diffused Multipath
        5. 14.5. Modeling Multipath Fading Channels
        6. 14.6. Random Process Models
          1. 14.6.1. Models for Temporal Variations in the Channel Response (Fading)
          2. 14.6.2. Important Parameters
            1. Multipath Spread
            2. Doppler Bandwidth
        7. 14.7. Simulation Methodology
          1. 14.7.1. Simulation of Diffused Multipath Fading Channels
            1. Special Cases
            2. Sampling
            3. Generation of Tap Gain Processes
            4. Delay Power Profiles and Doppler Power Spectral Densities
            5. Correlated Tap Gain Model
          2. 14.7.2. Simulation of Discrete Multipath Fading Channels
            1. Uniformly Spaced TDL Model for Discrete Multipath Fading Channels
          3. 14.7.3. Examples of Discrete Multipath Fading Channel Models
            1. Rummler’s Model for LOS Terrestrial Microwave Channels
            2. Models for Mobile Channels
              1. Discrete Channel Models for GSM Applications
              2. Discrete Models for PCS Applications
              3. Discrete Multipath Channel Models for 3G Wideband CDMA Systems
          4. 14.7.4. Models for Indoor Wireless Channels
        8. 14.8. Summary
        9. 14.9. Further Reading
        10. 14.10. References
        11. 14.11. Problems
        12. 14.12. Appendix A: MATLAB Code for Example 14.1
          1. 14.12.1. Main Program
          2. 14.12.2. Supporting Functions
        13. 14.13. Appendix B: MATLAB Code for Example 14.2
          1. 14.13.1. Main Program
          2. 14.13.2. Supporting Functions
            1. jakes_filter.m
            2. linear_psd.m
      4. 15. Discrete Channel Models
        1. 15.1. Introduction
        2. 15.2. Discrete Memoryless Channel Models
        3. 15.3. Markov Models for Discrete Channels with Memory
          1. 15.3.1. Two-State Model
          2. 15.3.2. N-state Markov Model
          3. 15.3.3. First-Order Markov Process
          4. 15.3.4. Stationarity
          5. 15.3.5. Simulation of the Markov Model
        4. 15.4. Example HMMs—Gilbert and Fritchman Models
        5. 15.5. Estimation of Markov Model Parameters
          1. 15.5.1. Scaling
          2. 15.5.2. Convergence and Stopping Criteria
          3. 15.5.3. Block Equivalent Markov Models
        6. 15.6. Two Examples
        7. 15.7. Summary
        8. 15.8. Further Reading
        9. 15.9. References
        10. 15.10. Problems
        11. 15.11. Appendix A: Error Vector Generation
          1. 15.11.1. Program: c15_errvector.m
          2. 15.11.2. Program: c15_hmmtest.m
        12. 15.12. Appendix B: The Baum-Welch Algorithm
        13. 15.13. Appendix C: The Semi-Hidden Markov Model
        14. 15.14. Appendix D: Run-Length Code Generation
        15. 15.15. Appendix E: Determination of Error-Free Distribution
          1. 15.15.1. c15_intervals1.m
          2. 15.15.2. c15_intervals2.m
      5. 16. Efficient Simulation Techniques
        1. 16.1. Tail Extrapolation
        2. 16.2. pdf Estimators
        3. 16.3. Importance Sampling
          1. 16.3.1. Area of an Ellipse
            1. Monte Carlo Estimators Revisited
            2. Selecting Bounding Boxes for MC Simulations
            3. Optimal Bounding Regions
            4. Nonuniform pdfs and Weighting Functions
          2. 16.3.2. Sensitivity to the pdf
          3. 16.3.3. A Final Twist
          4. 16.3.4. The Communication Problem
          5. 16.3.5. Conventional and Improved Importance Sampling
        4. 16.4. Summary
        5. 16.5. Further Reading
        6. 16.6. References
        7. 16.7. Problems
        8. 16.8. Appendix A: MATLAB Code for Example 16.3
          1. 16.8.1. Supporting Routines
            1. cgpdf.m
      6. 17. Case Study: Simulation of a Cellular Radio System
        1. 17.1. Introduction
        2. 17.2. Cellular Radio System
          1. 17.2.1. System-Level Description
          2. 17.2.2. Modeling a Cellular Communication System
            1. Trunking and Grade of Service
            2. Channel Model
            3. Sectorized Cells
            4. Total Co-Channel Interference
            5. Effects of Sectoring
        3. 17.3. Simulation Methodology
          1. 17.3.1. The Simulation
            1. Definition of the Target System to Be Simulated
              1. Propagation characteristics (channel parameters)
              2. Locations of co-channel cells
            2. Generation of Snapshots of Mobiles’ Locations and Computation of SIR
              1. Step 1: A mobile is placed within each cell
              2. Step 2: Determination of the distances between mobiles and base stations
              3. Step 3: Determination of the statistics of SIR on both links
          2. 17.3.2. Processing the Simulation Results
            1. Outage Probability
            2. System Performance over the Cell Area
        4. 17.4. Summary
        5. 17.5. Further Reading
        6. 17.6. References
        7. 17.7. Problems
        8. 17.8. Appendix A: Program for Generating the Erlang B Chart
        9. 17.9. Appendix B: Initialization Code for Simulation
        10. 17.10. Appendix C: Modeling Co-Channel Interference
          1. 17.10.1. Wilkinson’s Method
          2. 17.10.2. Schwartz and Yeh’s Method
        11. 17.11. Appendix D: MATLAB Code for Wilkinson’s Method
      7. 18. Two Example Simulations
        1. 18.1. A Code-Division Multiple Access System
          1. 18.1.1. The System
          2. 18.1.2. The Simulation Program
          3. 18.1.3. Example Simulations
            1. Baseline Validation
            2. Performance as a Function of Eb/N0 and the Ricean K-factor
          4. 18.1.4. Development of Markov Models
            1. Program 1: c18_cdmahmm1.m
            2. Program 2: c18_cdmahmm2.m
            3. Program 3: c18_cdmahmm3
        2. 18.2. FDM System with a Nonlinear Satellite Transponder
          1. 18.2.1. System Description and Simulation Objectives
          2. 18.2.2. The Overall Simulation Model
          3. 18.2.3. Uplink FDM Signal Generation
          4. 18.2.4. Satellite Transponder Model
          5. 18.2.5. Receiver Model and Semianalytic BER Estimator
          6. 18.2.6. Simulation Results
            1. Baseline Validation
            2. Nonlinear and Noise Effects −5 FDM Carriers
          7. 18.2.7. Summary and Conclusions
        3. 18.3. References
        4. 18.4. Appendix A: MATLAB Code for CDMA Example
          1. 18.4.1. Supporting Functions
            1. MSquence.m
            2. LinearFeedbackShiftRegiater,m
        5. 18.5. Appendix B: Preprocessors for CDMA Application
          1. 18.5.1. Validation Run
          2. 18.5.2. Study Illustrating the Effect of the Ricean K-Factor
        6. 18.6. Appendix C: MATLAB Function c18_errvector.m
        7. 18.7. Appendix D: MATLAB Code for Satellite FDM Example
          1. 18.7.1. Supporting Functions
            1. mpsk_impulses.m
            2. sqrc_time.m
            3. sqrc_freq_nosinc.m
            4. delayr1.m
            5. twt_model.m
            6. twtdata1.m
      8. About the Authors

    Product information

    • Title: Principles of Communication Systems Simulation with Wireless Applications
    • Author(s): K. Sam Shanmugan, Theodore S. Rappaport, William H. Tranter, Kurt L. Kosbar
    • Release date: December 2003
    • Publisher(s): Pearson
    • ISBN: 0134947908