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Data Communications Principles de Richard D. Gitlin

Data Communications Principles: Applications of Communications Theory

Autor Richard D. Gitlin, Jeremiah F. Hayes, Stephen B. Weinstein
en Limba Engleză Paperback – 27 sep 2012
This unique text, for both the first year graduate student and the newcomer to the field, provides in-depth coverage of the basic principles of data communications and covers material which is not treated in other texts, including phase and timing recovery and echo cancellation. Throughout the book, exercises and applications illustrate the material while up-to-date references round out the work.
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Specificații

ISBN-13: 9781461364481
ISBN-10: 1461364485
Pagini: 756
Ilustrații: XIX, 733 p.
Dimensiuni: 155 x 235 x 40 mm
Greutate: 1.04 kg
Ediția:Softcover reprint of the original 1st ed. 1992
Editura: Springer Us
Colecția Springer
Seria Applications of Communications Theory

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

1. Introduction to Data Communications.- 1.0 A Perspective.- 1.1 Who Uses Data Communication?.- 1.2 Data Network Protocols.- 1.3 Data Network Architectures.- 1.4 Data Communication at Voiceband Rates.- 1.5 Carrier Systems.- 1.6 Channel Characterizations.- 1.7 Signal Processing for Data Communications.- 1.8 Organization of This Book.- References.- 2. Theoretical Foundations of Digital Communications.- 2.0 Introduction.- 2.1 Introduction to Decision Theory.- 2.2 The Additive White Gaussian Noise (AWGN) Channel.- 2.3 The Binary Symmetric Channel.- 2.4 Elements of Estimation Theory.- 2.5 Fundamentals of Information Theory.- 2.6 Channel Capacity.- 2.7 Calculations of Channel Capacity for Selected Channels.- Appendix 2A: Basic Concepts of Probability Theory.- Appendix 2B: Detection of Signals in Colored Noise.- References.- Exercises.- 3. Error Correcting and Detecting Codes.- 3.0 Introduction.- 3.1 Block Codes.- 3.2 Cyclic Block Codes.- 3.3 Performance.- 3.4 Convolutional Codes.- 3.5 Decoding Convolutional Codes—The Viterbi Algorithm.- 3.6 Performance of Convolutional Codes.- 3.7 Sequential Decoding Convolutional Codes.- 3.8 Block and Convolutional Codes Concatenated.- 3.9 Automatic Repeat-Request Systems.- References.- Exercises.- 4. Baseband Pulse Transmission.- 4.0 Introduction.- 4.1 Direct-Baseband Transmission.- 4.2 Pulse Amplitude Modulation (PAM) in a Distorted, Noisy, Bandlimited Channel.- 4.3 The Nyquist Criterion.- 4.4 Performance of Multilevel PAM with Raised Cosine Pulse Shaping.- 4.5 General Encoding Model.- 4.6 Correlative Level Encoding (Partial Response).- 4.7 Block Codes: A Multirate Digital Filtering Approach.- 4.8 Signaling on the Digital Subscriber Access Line.- 4.9 Intersymbol Interference.- Appendix 4A: Power Density Function of a Correlated LineSignal.- References.- Exercises.- 5. Passband Data Transmission.- 5.0 Introduction.- 5.1 Complex Analytic Representations.- 5.2 Linear Modulation Formats.- 5.3 Direct Inband Signal Generation.- 5.4 Multitone Data Transmission.- 5.5 Higher-Dimensional Signaling.- 5.6 Frequency-Shift Keying.- 5.7 Trellis-Coded Modulation.- 5.8 Conclusion.- References.- Exercises.- 6. Synchronization: Carrier and Timing Recovery.- 6.0 Introduction.- 6.1 Optimum (Maximum Likelihood) Carrier Phase Estimation.- 6.2 The Phase-Locked Loop (PLL).- 6.3 Carrier Recovery: Non-Data-Aided Systems.- 6.4 Carrier Recovery: Data-Aided Systems.- 6.5 Timing Recovery.- 6.6 Joint Carrier and Timing Recovery.- 6.7 Periodic Inputs and Scramblers.- References.- Exercises.- 7. Optimum Data Transmission.- 7.0 Introduction.- 7.1 Maximum Likelihood Sequence Estimation (MLSE): The Viterbi Algorithm.- 7.2 Whitened Matched Filter Receiver.- 7.3 Suboptimum MLSE Structures.- 7.4 The Optimum Linear Receiver (Equalizer).- 7.5 Decision Feedback Equalization.- 7.6 Chapter Summary.- Appendix 7A: The Wiener—Hopf Decision Feedback Equation.- References.- Exercises.- 8. Automatic and Adaptive Equalization.- 8.0 Introduction.- 8.1 Scope of Equalization Applications.- 8.2 Baseband Equivalent System.- 8.3 Minimization of the Mean-Square Error by the Gradient Algorithm.- 8.4 The Least-Mean-Square (LMS) Estimated-Gradient Algorithm.- 8.5 Fast Convergence via the Kalman (Recursive Least-Squares) Algorithm.- 8.6 Fast Kalman Algorithms: Kalman Algorithms with Reduced Complexity.- 8.7 Lattice Filters: Another Structure for Fast-Converging Equalization.- 8.8 Tracking Properties of the LMS and the Recursive Least-Squares Algorithms.- 8.9 Complexity Comparison.- 8.10 Cyclic Equalization.- 8.11 Zero-Forcing Equalization.- 8.12 PassbandEqualization.- 8.13 Joint Optimization of Equalizer Tap Coefficients and Demodulation Phase.- 8.14 Adaptive Cancellation of Intersymbol Interference.- 8.15 Blind Equalization.- 8.16 Chapter Summary.- Appendix 8A: Convexity of the Mean-Square Error.- Appendix 8B: Asymptotic Eigenvalue Distribution for the Correlation Matrix of Synchronous and Fractionally-Spaced Equalizer.- Appendix 8C: Derivation of the Matrix Inversion Lemma.- Appendix 8D: Tracking Properties of the LMS and RLS Algorithms.- References.- Exercises.- 9. Echo Cancellation.- 9.0 Introduction.- 9.1 The Dialed Telephone Circuit Echo Cancellation Model.- 9.2 The Echo Cancellation Model for Digital Subscriber Lines.- 9.3 FIR (Tapped Delay Line) Canceler Structures.- 9.4 Other Canceler Structures.- 9.5 Passband Considerations.- References.- Exercises.- 10. Topics in Digital Communications.- 10.0 Introduction.- 10.1 Effect of Digital Implementation on the Performance of Adaptive Equalizers.- 10.2 Adaptive Carrier Recovery Systems.- 10.3 Signal Processing for Fiber-Optic Systems.- Appendix10A: A Comparison of the Quantization Error (QE) of a Fixed Equalizer with the Achievable Digital Residual Error (DRE) of an Adaptive Equalizer.- Appendix10B: The Effect of Linear Equalization on Quadratic Distortion for Lightwave Systems.- References.