Principles of CS-Based Sparse Channel Estimation Algorithm and MATLAB Implementation

In this document, we will discuss the principles of compressed sensing (CS)-based sparse channel estimation algorithms and provide corresponding MATLAB program examples. Sparse channel estimation is a methodology for estimating channel characteristics that leverages compressed sensing techniques to achieve accurate estimation when the channel contains only a small number of non-zero coefficients. We will thoroughly examine the working mechanism of this algorithm and demonstrate how to implement it using MATLAB programming. The implementation typically involves key functions such as measurement matrix design (e.g., random Gaussian matrices), sparse recovery algorithms (like OMP, CoSaMP, or L1-minimization), and performance evaluation metrics. Through MATLAB examples, we will show practical aspects including signal sampling, dictionary construction, and reconstruction error analysis. This document will enable readers to gain a deeper understanding of CS-based sparse channel estimation algorithms and develop the capability to implement practical applications using MATLAB.