Improved Compressed Sensing-Based Sparse Channel Estimation Method for Broadband Communication System

In this paper, we investigate various channel estimators that exploiting channel sparsity for broadband frequency-selective communication system. As the wireless channels tend to exhibit sparse structure in high-dimensional space, e.g., delay spread, Doppler spread and space spread. Some sparse channel estimators have been proposed based on the novel theory of compressed sensing. For convex relaxation algorithms, namely LASSO and DS, they turn to solve the convex optimization problem. For greedy algorithms, in form of compressive sampling matching pursuit (CoSaMP) algorithm, refine the sparse solution iteratively. Numerical simulations are used to evaluate the proposed algorithms in comparison to the conventional least-squares (LS) channel estimator. We observe that the convex relaxation algorithms outperform the LS method for the sparse multipath channel. On the other hand, the greedy algorithms led by CoSaMP uniformly outperform the LS and convex approach.