Research On Channel Estimation Algorithm In MIMO-OFDM Systems

Orthogonal Frequency Division Multiplexing(OFDM)technology can resist frequency selective fading and improve spectrum utilization.Multiple Input Multiple Output(MIMO)technology can improve system capacity through diversity and multiple antenna technologies without increasing bandwidth.MIMO-OFDM technology is a fusion of MIMO technology and OFDM technology,which can achieve high rate and large capacity data transmission with limited spectrum resources.Therefore,MIMO-OFDM technology has become a hot research topic in wireless communication technology.In addition,accurate channel estimation can improve the system's performance in MIMOOFDM system,so it is important to research the channel estimation technology.Therefore,this thesis has researched the channel estimation algorithm deeply in MIMO-OFDM system.The main work and contributions of this thesis are as follows:Firstly,the OFDM,MIMO and MIMO-OFDM systems are introduced.Then the pilot pattern,estimation algorithm and interpolation algorithm are analyzed in detail.In this part,the emphasis is the summary of the LS,MMSE,DFT and DCT algorithms.Secondly,in the traditional DFT algorithm,the noise outside the cyclic prefix is processed,while the noise inside the cyclic prefix is not processed,moreover,it has a problem of energy leakage during non-interval sampling.This thesis proposes a channel estimation method combined with DWT and DCT method.This method improves the threshold of wavelet threshold denoising in the first step.Nextly,it uses the improved wavelet threshold denoising to denoise the frequency domain response obtained by LS coarse estimation.Finally,aiming at the signal about energy characteristics in the cyclic prefix after IDCT transformation,a threshold decision method is proposed to remove the noise in the cyclic prefix.The simulation results show that the proposed method can suppress the noise in the cyclic prefix and solve the energy leakage problem well.Compared with other algorithms,it also has a better performance on estimation.Thirdly,for the MIMO-OFDM sparse channel,this thesis proposes a channel estimation method based on Compressing Sampling,and mainly improves the Compressing Sampling Matching Pursuit(CoSaMP)algorithm.The original CoSaMP algorithm not only needs to know the sparseness of the original signal in advance,but also selects surperbundant atoms that is not all useful in the preselection stage,which increases the complexity of the algorithm.Focusing on those difficulties,in the one hand,we used variable step to approach the actual sparseness gradually in adaptive way.In the other hand,we used atomic weak selection criteria to select atoms flexibly.The simulation results show that the proposed method has better reconstruction accuracy and estimation performance,and it does not need to know the sparsity in advance,Therefor,the method this thesis refers to is more suitable for practical applications.