Implementation of Binary Sequence Generator with Power Spectrum and Autocorrelation Function Analysis
Implementation of Binary Sequence Generator with Power Spectrum and Autocorrelation Function Analysis
Generation of a Random Signal and Two Sinusoidal Signals with Closely-Spaced Frequencies
Generate a random signal and two sinusoidal signals with different but closely-spaced frequencies, then perform comprehensive signal analysis including: (1) calculating autocorrelation coefficients and correlation functions with corresponding plots; (2) computing power spectra using different parametric modeling methods; (3) estimating parameters for AR, MA, and ARMA models using maximum likelihood estimation and recursive least squares, with comparison to MATLAB toolbox functions; (4) spectral estimation using notch filtering and MUSIC methods; (5) noise reduction using Wiener and LMS filtering
GMSK Modulation and Demodulation
GMSK modulation and demodulation in an ideal channel with power spectral density visualization.
Baseband Input Waveform and Its Power Spectrum, AWGN Channel Output and Its Power Spectrum, Signal Constellation Before and After Rayleigh Channel
This content covers: 1. Baseband input waveform and its power spectral density 2. QPSK modulated signal and its power spectrum 3. AWGN channel output and its power spectrum 4. Signal constellation diagrams before and after Rayleigh channel transmission 5. Bit error rate performance under AWGN and Rayleigh fading channel conditions with theoretical AWGN curves, all BER curves plotted using identical coordinate scales
Feature Vectors and Power Spectra of Frequency Components using Wavelet Packet Transform
Analyzing feature vectors and power spectra of frequency components for two signals through wavelet packet transform implementation
Power Spectrum Estimation of Signals Using Fourier Transform
Various methods for power spectrum estimation via Fourier Transform: Periodogram method, Modified Periodogram with segmentation, Welch's method for reduced variance, Multitaper Method (MTM) using multiple orthogonal windows, Maximum Entropy Method (MEM) for high-resolution estimation, and Multiple Signal Classification (MUSIC) for frequency detection. Implementation approaches include FFT computation, windowing functions, and spectrum averaging techniques.
Maximum Signal-to-Noise Ratio Criterion Patterns and Power Spectra
Maximum SNR criterion patterns and power spectra; ASC sidelobe cancellation using MSE criterion; Linearly Constrained Minimum Variance (LCMV) criterion; Capon beamforming with different covariance matrix estimation methods; Multipoint constrained Capon beamforming and pattern synthesis
16QAM Modulation with Root-Raised Cosine Filter
Implementation of 16QAM signals with root-raised cosine roll-off factor of 0.35, including customizable parameters and power spectral density analysis. This MATLAB-based solution demonstrates digital modulation techniques with configurable filter characteristics.
MATLAB Code for White Noise Power Spectrum Analysis
Ready-to-run MATLAB implementation for white noise power spectrum calculation, ideal for beginners learning signal processing concepts with practical code examples