Compressed Sensing Channel Estimation Algorithm (MATLAB Implementation)

This article explores the principles and implementation of compressed sensing channel estimation algorithms. Compressed sensing represents an emerging signal processing technique capable of reconstructing original signals from limited observational data. We demonstrate MATLAB code implementation for compressed sensing channel estimation, providing comprehensive explanations of each algorithmic step's functionality and significance. The implementation typically involves key components such as sparse signal representation using orthogonal matching pursuit (OMP) algorithms, measurement matrix design using random Gaussian matrices, and signal reconstruction through l1-norm optimization techniques. This approach enables more accurate channel state estimation, thereby enhancing communication system performance. The MATLAB implementation includes functions for generating measurement matrices, applying sparse recovery algorithms, and evaluating estimation accuracy through mean squared error calculations. This content aims to facilitate understanding of compressed sensing channel estimation fundamentals and their practical application in real-world communication scenarios.