Explore MATLAB source code curated for "LS" with clean implementations, documentation, and examples.
Analysis and comparison of various channel estimation methods for MIMO-OFDM systems, including Least Squares (LS), Linear Minimum Mean Square Error (LMMSE), and Discrete Fourier Transform (DFT) approaches, focusing on their implementation algorithms and performance characteristics.
Channel estimation simulation based on block-type pilots, featuring implementation of Least Squares (LS) and Linear Minimum Mean Square Error (LMMSE) channel estimation algorithms with MATLAB code descriptions.
Comprehensive simulation of OFDM channel estimation techniques including Least Squares (LS) and Minimum Mean Square Error (MMSE) algorithms with complete MATLAB implementation
Simulation code implementing compressed sensing-based channel estimation with performance comparison against conventional Least Squares (LS) and Minimum Mean Square Error (MMSE) algorithms, featuring MATLAB/Octave implementations of sparse recovery techniques and statistical error analysis
Multiple SNR estimation algorithms including Least Squares (LS), Least Mean Squares (LMS), Quadrature Amplitude Modulation (QAM), and noise statistics approaches
Simulation of OFDM channel estimation using block-type pilot arrangement, implementing both LS (Least Squares) and LMMSE (Linear Minimum Mean Square Error) estimation algorithms with MATLAB code demonstrations.
This code implements a transform-domain estimation algorithm for OFDM systems, featuring comprehensive OFDM system simulation along with enhanced channel estimation algorithms. The implementation includes LS, MMSE, LMMSE, DFT, and an improved DCT-based algorithm with detailed MATLAB simulations demonstrating performance comparisons.
Active Approach - Time of Arrival (TOA) Least Squares (LS) Localization Method
Performance comparison of Least Squares (LS) and Minimum Mean Square Error (MMSE) estimators validated against theoretical curves with implementation analysis