An analysis of CS algorithms efficiency for sparse communication signals reconstruction

As need for increasing the speed and accuracy of the real applications is constantly growing, the new algorithms and methods for signal processing are intensively developing. Traditional sampling approach based on Sampling theorem is, in many applications, inefficient because of production a large number of signal samples. Generally, small number of significant information is presented within the signal compared to its length. Therefore, the Compressive Sensing method is developed as an alternative sampling strategy. This method provides efficient signal processing and reconstruction, without need for collecting all of the signal samples. Signal is sampled in a random way, with number of acquired samples significantly smaller than the signal length. In this paper, the comparison of the several algorithms for Compressive Sensing reconstruction is presented. The one dimensional band-limited signals that appear in wireless communications are observed and the performance of the algorithms in non-noisy and noisy environments is tested. Reconstruction errors and execution times are compared between different algorithms, as well.