Design And Improvement Of CS-based Software-defined UWB Receiver

Ultra-wideband(UWB)technology has become an important alternative for short-range wireless communication due to its high transmission rate and low power consumption.High-speed analog-to-digital conversion is an important factor restricting its development.In recent years,the theory of compressed sensing(CS)has attracted extensive attention.Based on the sparse characteristics of UWB signals,the use of compressed sensing can greatly reduce the sampling rate,which provides a new research direction for UWB technology.Based on the software-defined radio(SDR)platform,using software algorithms to complete most of the functions of the transceiver is one of the trends in the development of communication technology.However,the compressed sensing differs a lot from the Nyquist sampling theory in sampling and reconstruction mechanisms.The traditional SDR architecture and algorithms cannot be applied to CS-based ultra-wideband systems.Therefore,combined with the theory of compressed sensing,it is of great value to study the architecture and algorithms of new ultra-wideband receivers.In this thesis,based on the concept of SDR and CS low-rate sampling,the impulse radio ultra-wideband(IR-UWB)receiver architecture and related signal processing and symbol detection algorithms are studied.The research results are mainly reflected in the following aspects:1.Aiming at the IR-UWB technology,a software-defined receiver architecture based on CS is proposed,and the signal acquisition and processing flow of the receiver is illustrated.The receiver uses compressed sensing to achieve sampling and quantization of pilot and data symbols,and implements channel estimation,data signal pre-processing,diversity combining and symbol detection through software algorithms The receiver's performance in a typical channel environment is verified by simulation,the factors restricting the receiver's performance are analyzed,and possible measures to improve the receiver's performance are pointed out.2.In order to overcome the influence of noise,thinking from the perspective of channel estimation and data signal preprocessing algorithms,an improved scheme based on pre-selected support set and optimal support set is proposed.Based on the prior knowledge of UWB signal statistics,the pre-selected support set is determined,the pre-selected support set and the improved channel estimation algorithm are used to improve the channel estimation accuracy,and then the preferred support set is determined according to the channel estimation result,and the preferred support set and the improved data signal preprocessing algorithm are used to improve the preprocessing performance.The simulation results show that the improved scheme can effectively suppress the influence of noise and improve the performance of the receiver in bit error rate(BER).At the same time,the improved scheme has lower computational complexity.3.In order to suppress the noise folding of compressed sensing,a noise-resistant measurement matrix is used instead of a random measurement matrix for compressed sensing,and two improved schemes based on the noise-resistant measurement matrix are proposed.The simulation results show that the BER performance of the two improved schemes is significantly improved and can achieve the same performance as that of the related receivers.