Compressed Sensing Application Research In Uwb Communication System

Compressive sensing(CS) theory is of concern in the signal processing field, while ultra-wideband(UWB) wireless communication technology is also a hot research topic in the modern communication field nowadays. The new point in the compressive sensing is that it compresses the information while this is sampled. For the analog signals, especially the signal with extremely wide bandwidth processing systems, using CS theory can greatly reduce the sampling rate. The demanding of ultra-wideband communications include extremely high sampling rates in the receiver because of the signal with very wide band. This thesis starts with the problems which both of these theories can solve and to be solved, and then compressive sensing is applied to ultra-wideband communication system, which is aimed to reduce the sampling rate of receivers, and has important theoretical and practical value.This thesis first introduces the research background, including the development and research actuality of the compressive sensing theory and the UWB technology. Secondly, the significance of the combination of them is given. And then, the basic principle of compressed sensing theory is analyzed. We focus on signal sparse representation including the mathematical definition and how to build sparse matrix, design of measurement matrix including the conditions that the matrix must satisfy and reconstruction algorithm. The key of the UWB technology is introduced, which is includes how the UWB signal generates, the channel model and the receiver.The key point of this thesis is to apply compressed sensing in sampling of UWB. We first analyze the sparse of the UWB signal and explore two technical solutions to construct the CS model for UWB communication, one takes several parallel correlator and integrator to process the received signal in order to get M measurements and the other exploits the channel itself as a sensing process. Both of them are transformed to the model as the problem compressed sensing can solved. At last, we also apply the CS model to the UWB channel estimation, and propose channel estimation using coherent technology base training sequence and using random filter base UWB pulse.We can get the conclusion that applying compressed sensing to the ultra-wide band communications has many advantages because of the results of theoretical analyze and MATLAB simulation.