There exist many textbooks that provide an in-depth treatment of various topics in digital modulation techniques. Most of them underscore different theoretical aspects of design and performance analysis of digital modulation techniques. Only a handful of books provide insight on how these techniques can be modeled and simulated. Predominantly, such books utilize the sophisticated built-in functions or toolboxes that are already available in software like Matlab. These built-in functions or toolboxes hide a lot of background computations from the user thereby making it difficult, especially for a learner, to understand how certain techniques are actually implemented inside those functions. In this book shows the theoretical aspects of how a digital modulation-demodulation system can be translated into simulation models, using existing packages in Python3 (Python version 3).
Author(s): Mathuranathan Viswanathan
Edition: 1
Year: 2019
Language: English
Pages: 216
Tags: Software
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
References
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 non-coherent 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
References
Digital Modulators and Demodulators - Complex Baseband Equivalent Models
Introduction
Complex baseband representation of 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
References
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
References
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
References
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
References
