Research On Fast Time-varying Channel Estimation Based On BEM

For a fast time-varying channel,its channel parameters will change rapidly in a short time,which makes the channel estimation of fast time-varying channel need to estimate a large number of unknown parameters continuously.Scholars have found that the Basis Expansion Model can capture the time-varying characteristics of the channel,and can effectively simulate the transmission of the channel,and then is often used in time-varying channel model.Based on the idea of the Basis Expansion Model,this thesis researches the fast time-varying channel and proposes a new channel estimation method.The main contents are as follows:1.Chapter 3 using LS algorithm to study the channel estimation based on BEM,a channel estimation method based on BEM channel model combining Recursive Least Squares algorithm with LS algorithm is proposed.Firstly,the channel transmission information is obtained by using the Basis Expansion Model,then the initial value of the base coefficient in the RLS algorithm is obtained by using the LS algorithm,and the first adaptive filter is used for parallel recurrence,and the base coefficient tracking update transformation is used by the RLS algorithm,and finally the base coefficient estimate is obtained.The simulation results show that the proposed algorithm is compared with the traditional LS algorithm.The new algorithm has better estimation accuracy.2.In chapter 4,a Central Difference Kalman Filter algorithm is proposed to estimate the base coefficient indirectly by analyzing the Basis Expansion Model and the fast time-varying channel.Firstly,according to the Kalman Filter theory,the state transfer equation about the base coefficient is established by using the low order AR model for the estimation of the base coefficient,and the output relation of the OFDM system based on the Basis Expansion Model is used as the prediction equation,and then the state space model is established according to the derived nonlinear relationship.Finally,we use this state space model to estimate the basis coefficient indirectly.By simulating the proposed algorithm with LS algorithm and Extended Kalman Filter algorithm Contrastive experiments.The results show that the proposed method has better performance than the latter two algorithms.3.In chapter 5,by analyzing the characteristics of fast time-varying channel,a new time-varying sparse channel estimation and tracking method is proposed.Firstly,the SAMP algorithm is used to detect the delay,the delay position of the main path is found,and then the column set corresponding to the delay of the main path is used by Fourier matrix F to construct therequired Fourier matrix in the transmission model of BEM based OFDM system.Finally,the KF algorithm is used to estimate the base coefficient,and then the channel response is estimated.The simulation results show that the proposed algorithm has better estimation accuracy than the combination of SAMP and LS and the combination of SAMP and LMMSE.