Explore MATLAB source code curated for "高斯白噪声" with clean implementations, documentation, and examples.
Implementation of mean filtering for Gaussian white noise removal in MATLAB, median filtering for noise reduction, and frequency domain low-pass filtering using ideal low-pass filters with code examples and algorithm explanations.
Implementation of MPSK modulation with transmission models using rectangular, raised cosine, and square-root raised cosine pulse shaping. Supports M-values of 2, 4, and 8 with additive white Gaussian noise (AWGN) channel simulation.
RLS adaptive filter program for system identification with Gaussian white noise input, featuring detailed code comments and immediate execution capability. The implementation demonstrates real-time parameter adaptation using recursive least squares algorithm.
Source code for Gaussian white noise algorithm with MATLAB implementation for communication system programming
This project provides simulation of bit error rate performance under AWGN wireless communication channels, offering valuable learning resources with MATLAB/Python implementation examples and algorithm explanations.
Simulation program for MUSIC algorithm implementation in smart antenna uplink systems, featuring narrowband signal processing in additive white Gaussian noise (AWGN) environments with code-level performance analysis
A comprehensive BPSK bit error rate simulation program featuring signal generation, additive white Gaussian noise (AWGN) integration, and BER calculation algorithms
Comparative Bit Error Rate Analysis of BPSK and 16-QAM modulation techniques over AWGN channels, including receiver implementation and comprehensive examination of 16-QAM modulation/demodulation principles with MATLAB code implementation examples
Implementation of LMS algorithm for noise removal, where the clean signal comprises various digital modulation signals (specifically 8ASK digital amplitude modulation) and the noise is additive white Gaussian noise with customizable signal-to-noise ratio.
This MATLAB function generates Gaussian white noise through customizable parameter settings, enabling users to create specific white noise signals while providing insights into random signal generation methodologies.