Channel Estimation Based on Comb-Type Pilots in MIMO-OFDM Systems
MIMO-OFDM channel estimation using comb-type pilots with least squares method and linear interpolation for accurate channel state information recovery.
Explore MATLAB source code curated for "信道估计" with clean implementations, documentation, and examples.
MIMO-OFDM channel estimation using comb-type pilots with least squares method and linear interpolation for accurate channel state information recovery.
Subspace-based channel estimation technique for SISO-OFDM systems with ready-to-execute implementation featuring matrix decomposition and pilot signal processing algorithms
Channel estimation based on Least Squares (LS) and Minimum Mean Square Error (MMSE) approaches, featuring detailed simulation results including Bit Error Rate and Mean Square Error performance metrics.
MATLAB simulation program for channel estimation in ultra-wideband systems using Multi-Band Orthogonal Frequency Division Multiplexing (MB-OFDM) technology
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);
This article provides a comprehensive comparison of three fundamental channel estimation algorithms for OFDM systems: Least Squares (LS), Minimum Mean Square Error (MMSE), and Linear Minimum Mean Square Error (LMSE), including their performance characteristics, computational complexity, and implementation considerations.
MATLAB implementation featuring QPSK modulation/demodulation with serial-to-parallel conversion, FFT/IFFT operations, channel estimation using Least Squares (LS) criterion, and Doppler shift simulation - providing clear modular demonstration of digital communication principles.
Source code implementation for traditional pilot-based channel estimation in OFDM systems, featuring key algorithms and signal processing techniques
MATLAB-based MIMO-OFDM channel estimation comparison program with excellent performance, featuring comprehensive algorithm analysis and performance evaluation capabilities.
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.