Enhancing DSSS-signals channel estimation through compressive sensing

A new approach for channel estimation, based on the concept of compressive sensing is presented for situations where signals are DSSS transmitted. Compressive sensing encompasses elegant mathematical background where the information of the sparse characteristic of the signal is used to create an efficient representation of it using fewer measurements than required by the Shannon-Nyquist criterion. The reconstruction is performed through linear programming or greedy algorithms. In this paper, we demonstrate that compressive sensing can enable reduction in the analog-to-digital sampling rate, allowing the design of simpler and cheaper receivers, while keeping or improving resolution in the DSSS channel estimation.