Research On Wireless Channel Estimation Based On Adaptive Distributed Compressed Sensing Reconstruction Algorithm

As a new method of signal processing technology,Compressive Sensing(CS)can simultaneously perform data acquisition and compression.Its signal sampling rate is much lower than the traditional Nyquist sampling method,which can effectively solve the problems caused by big digital data processing.Based on the CS theory,Distributed Compressive Sensing(DCS)is developed which makes full use of both intra-and inter-signal correlation structures.DCS realizes simultaneous reconstruction of multiple sparse signals,which improves the efficiency of the overall reconstruction process.Compared with CS methods,DCS can achieve higher accuracy with faster speed in multi-signal reconstruction,which has a very promising application prospect.Therefore,DCS theory has attracted the attention of scholars at home and abroad.Reconstruction algorithm is a focus issue of the DCS,which directly affects the reconstruction effect of signals.However,most reconstruction algorithms need the sparsity of the known signals as a prior information,which is unrealized at the actual situations.Consequently,it's necessary to make research on adaptive DCS reconstruction algorithms.Orthogonal Frequency Division Multiplexing(OFDM)technology has been widely used in many communication fields due to its efficient bandwidth and strong anti-interference ability.OFDM systems often suffers from doubly-selective(DS)fading in practical communication,such as high-speed rail communication systems,underwater acoustic wireless networks.The channel model exhibits joint sparsity,which means that we can addresse the channel estimation problem from the perspective of DCS.This thesis focuses on adaptive DCS reconstruction algorithms and the application of the algorithm in DS fading OFDM system.The main contributions of this thesis are as follows.1)After studying a large number of traditional CS and DCS reconstruction algorithms,DCS-Improved Sparsity Adaptive Matching Pursuit(DCS-IMSAMP)is proposed based on the DCS-Sparsity Adaptive Matching Pursuit(DCS-SAMP).The advantage of the proposed algorithm is that it can exploit the joint channel sparsity information using dynamic threshold,variable step size and tailoring mechanism.Simulation results show that the proposed algorithm achieves 8d B performance gain with faster operation speed,in comparison with traditional DCS-SAMP algorithm.On the other hand,the initial step size of the DCS-IMSAMP can be taken in the range of [1,6] integers according to actual situations,and the reconstruction effects achieved are all better than the DCS-SAMP algorithm.2)From the perspective of DCS,the DS channel estimation problem based on basis expansion model(BEM)in OFDM systems is deeply studied.Combined with BEM,the fasting-vary channel taps are represented as a linear combination of a finite number of basis functions with invariant coefficients,which reduces the number of coefficients to be estimated.After mathematical transformation,the BEM-based channel model exhibits joint sparsity.Finally,in order to conform to DCS model,the arrangement of OFDM polits is studied.3)The DCS-Improved Sparsity Adaptive Matching Pursuit is applied to the DS fading OFDM channel estimation,realizing the high-performance of estimation under the unknown channel sparsity in the actual situation.Simulation results show that the proposed method of channel estimation achieves 5d B performance gain with less running time,in comparison with other algorithms.