Simulation of Channel Estimation Using Compressed Sensing

This simulation code implements channel estimation using compressed sensing techniques, applicable for wireless communication research. The research has been published in an IEEE conference, providing detailed exploration and analysis of compressed sensing applications in channel estimation. The implementation typically involves sparse signal recovery algorithms (such as L1-norm minimization or greedy algorithms like OMP) and carefully designed measurement matrices (e.g., random Gaussian matrices) to reduce required pilot signals. Research results demonstrate that compressed sensing achieves high accuracy and reliability in channel estimation, offering effective methods for optimizing and improving wireless communication systems. Through open-source sharing of this simulation code, we aim to further promote research and development in related fields, providing valuable references for both academic and industrial communities. The code structure includes modules for signal generation, measurement matrix design, reconstruction algorithms, and performance evaluation metrics like MSE and BER calculations.