Explore MATLAB source code curated for "信噪比" with clean implementations, documentation, and examples.
MIMO-OFDM Channel Estimation Comparison:%------------------------------------------ % EE359 final project, Fall 2002 % Channel estimation for a MIMO-OFDM system % By Shahriyar Matloub %------------------------------------------ clear all; %close all; i=sqrt(-1);
Evaluation metrics for image quality in image processing, including signal-to-noise ratio and other key indicators with code implementation approaches
Multivariate scattering correction algorithm for near-infrared scattering data processing, designed to enhance signal-to-noise ratio through advanced spectral preprocessing
This algorithm represents the classic Maximum Likelihood Estimation method for SNR estimation, which leverages the prior probability density function of the received channel to achieve accurate signal-to-noise ratio measurements. The ML approach demonstrates robust performance in estimating SNR through statistical optimization techniques, typically implemented via numerical methods like gradient ascent or expectation-maximization algorithms.
Implementing sliding correlation for pseudo-code acquisition and calculating detection probabilities across various signal-to-noise ratios (SNR) conditions.
This code performs Bit Error Rate (BER) simulations for both BPSK and QPSK modulation schemes across various Signal-to-Noise Ratio (SNR) values. The simulation transmits at least 10^6 bits for statistical reliability and evaluates performance across 10 different SNR iterations to generate comprehensive BER curves.
Beamforming implementation based on three criteria: Mean Square Error (MSE), Linearly Constrained Minimum Variance (LCMV), and Maximum Signal-to-Noise Ratio
Simulation of Gaussian channel with visualization of Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR) relationship using MATLAB
This program simulates Bit Error Rate (BER) and Mean Square Error (MSE) under varying Signal-to-Noise Ratio (SNR) conditions using block-type pilots, with channel estimation implemented through both Least Squares (LS) method and an enhanced Least Squares approach.
Simulation program for limited feedback systems, exploring scenarios where the number of feedback bits varies with changes in signal-to-noise ratio (SNR).