Digital Modulations using Python: (Color edition)

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). 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