Author: Mathuranathan Viswanathan
Editor (with publishing rights): Varsha Srinivasan
Formats : eBook and Paperback
Paperback: 216 pages
Publisher: Independently published (December 2019)
Language: English
ISBN: 978-1712321638 (paperback color print)
ISBN: 978-1712342749 (paperback black & white print)
Paperback Dimensions: 7 x 0.5 x 10 inches

Note: Only PDF version is available for purchase from this website. Paperback Print editions are available only from Amazon.

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Description

A learner-friendly, practical and example driven book, Digital Modulations using Python gives you a solid background in building simulation models for digital modulation systems in Python version 3. This book, an essential guide for understanding the implementation aspects of a digital modulation system, shows how to simulate and model a digital modulation system from scratch. The implemented simulation models shown in this book, provide an opportunity for an engineer to understand the basic implementation aspects of modeling various building blocks of a digital modulation system. It presents the key topics with required theoretical background along with the implementation details in the form of Python scripts.

Key features

► Basics of signal processing, essential for implementing digital modulation techniques – generation of test signals, interpreting FFT results, power and energy of a signal, methods to compute convolution, analytic signal and applications.
► Waveform and complex baseband equivalent simulation models.
► Digital modulation techniques covered: BPSK and its variants, QPSK and its variants, M-ary PSK, M-ary QAM, M-ary PAM, CPM, MSK, GMSK,M-ary FSK.
► Simulation for ascertaining performance of digital modulation techniques in AWGN and fading channels – Eb/N0 Vs BER curves.
► Design and implementation of linear equalizers – zero forcing and MMSE equalizers, using them in a communication link, LMS algorithm for adaptive equalization.
► Simulation and performance of modulation systems with receiver impairments.
► Examples using object oriented programming.
► Simulation scripts using SciPy, Numpy and Matplotlib packages.

Table of contents

• Essentials of Signal Processing
  • Generating standard test signals
    • Sinusoidal signals
    • Square wave
    • Rectangular pulse
    • Gaussian pulse
    • Chirp signal
  • Interpreting FFT results – complex DFT, frequency bins and FFTShift
    • Real and complex DFT
    • Fast Fourier Transform (FFT)
    • Interpreting the FFT results
    • FFTShift
    • IFFTShift
    • Some observations on FFTShift and IFFTShift
  • Obtaining magnitude and phase information from FFT
    • Discrete-time domain representation
    • Representing the signal in frequency domain using FFT
    • Reconstructing the time domain signal from the frequency domain samples
  • Power spectral density
  • Power and Energy of a signal
    • Energy of a signal
    • Power of a signal
    • Classification of signals
    • Computation of power of a signal – simulation and verification
  • Polynomials, Convolution and Toeplitz matrices
    • Polynomial functions
    • Representing single variable polynomial functions
    • Multiplication of polynomials and linear convolution
    • Toeplitz Matrix and Convolution
  • Methods to compute convolution
    • Method 1 – Brute-Force Method
    • Method 2 – Using Toeplitz Matrix
    • Method 3 – Using FFT to compute convolution
    • Miscellaneous methods
  • Analytic signal and its applications
    • Analytic signal and Fourier Transform
    • Applications of analytic signal
  • Choosing a filter : FIR or IIR : understanding the design perspective
    • Design specification
    • General considerations in design
• Digital Modulators and Demodulators – Passband Simulation Models
  • Introduction
  • Binary Phase Shift Keying (BPSK)
    • BPSK transmitter
    • BPSK receiver
    • End-to-end simulation
  • Coherent detection of Differentially Encoded BPSK (DEBPSK)
  • Differential BPSK (D-BPSK)
    • Sub-optimum receiver for DBPSK
    • Optimum noncoherent receiver for DBPSK
  • Quadrature Phase Shift Keying (QPSK)
    • QPSK transmitter
    • QPSK receiver
    • Performance simulation over AWGN
  • Offset QPSK (O-QPSK)
  • π∕4-DQPSK
  • Continuous Phase Modulation (CPM)
    • Motivation behind CPM
    • Continuous Phase Frequency Shift Keying (CPFSK) modulation
    • Minimum Shift Keying (MSK)
  • Investigating phase transition properties
  • Power Spectral Density (PSD) plots
  • Gaussian Minimum Shift Keying (GMSK)
    • Pre-modulation Gaussian Low Pass Filter
    • Quadrature implementation of GMSK modulator
    • GMSK spectra
    • GMSK demodulator
    • Performance
  • Frequency Shift Keying (FSK)
    • Binary-FSK (BFSK)
    • Orthogonality condition for non-coherent BFSK detection
    • Orthogonality condition for coherent BFSK
    • Modulator
    • Coherent Demodulator
    • Non-coherent Demodulator
    • Performance simulation
    • Power Spectral Density
• Digital Modulators and Demodulators – Complex Baseband Equivalent Models
  • Introduction
  • Complex baseband representation of a modulated signal
  • Complex baseband representation of channel response
  • Implementing complex baseband modems using object oriented programming
    • Pulse Amplitude Modulation (M-PAM) modem
    • Phase Shift Keying Modulation (M-PSK) modem
    • Quadrature Amplitude Modulation (M-QAM) modem
    • Optimum detector on IQ plane using minimum Euclidean distance
    • M-ary Frequency Shift Keying modem
  • Instantiation of modems
• Performance of Digital Modulations over Wireless Channels
  • AWGN channel
    • Signal to Noise Ratio (SNR) definitions
    • AWGN channel model
    • Theoretical Symbol Error Rates
    • Unified Simulation model for performance simulation
  • Fading channels
    • Linear Time Invariant channel model and FIR filters
    • Simulation model for detection in flat Fading Channel
    • Rayleigh flat-fading channel
    • Rician flat-fading channel
• Linear Equalizers
  • Introduction
  • Linear Equalizers
  • Symbol-spaced linear equalizer channel model
  • Implementing equalizers using object oriented programming
  • Zero-forcing equalizer
    • Least squares solution
    • Noise enhancement
    • Design and simulation of zero-forcing equalizer
    • Drawbacks of zero-forcing equalizer
  • Minimum mean square error (MMSE) equalizer
    • Alternate solution
    • Design and simulation of MMSE equalizer
  • Equalizer delay optimization
  • BPSK modulation with zero-forcing and MMSE equalizers
  • Adaptive equalizer: Least mean square (LMS) algorithm
• Receiver Impairments and Compensation
  • Introduction
  • DC offsets and compensation
  • IQ imbalance model
  • IQ imbalance estimation and compensation
    • Blind estimation and compensation
    • Pilot based estimation and compensation
  • Visualizing the effect of receiver impairments
  • Performance of M-QAM modulation with receiver impairments