Super Resolution Channel Estimation with Spread Spectrum Signal and Atomic Norm Minimization

Abstract

One of the important tasks in wireless communications is propagation delay estimation. It has been found that the channel estimation accuracy of compressed sensing is superior to the existing method based on signal parameter estimation through MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT). However, there is a problem in the method that multipath delays may not be resolved if they span between the grids. This problem leads to inaccurate channel estimation. In this paper, we describe the Atomic Norm Minimization (ANM) that enables super resolution channel estimation. In additions, we propose ANM by using Spread Spectrum (SS) signal. In this study, we show new experiments using Gold code for Global Positioning System (GPS). We have also added a new experiment to check the performance of the ANM with SS signal.