Research On Time-varying Channel Estimation Method In OFDMA System For Future High-speed Mobile Scenarios

In recent years,due to China's rapid economic and scientific development,high-speed railways(HSR)and expressways have developed rapidly and widely.With the large-scale deployment of HSRs operating at speeds exceeding 300 km / h,wireless communication in the HSR environment has attracted more and more attention.Moreover,as one of the important deployments of fifth-generation(5G)mobile communication networks,HSR is expected to achieve a mobile speed of up to 500 km / h or higher,where it can provide users with a data rate of 150 Mbps or higher.However,in the higher speed HSR(vehicle speed> = 500 km / h)environment in the future,the high-speed operation of the train will cause a larger Doppler frequency shift,which will cause the channel to change rapidly.Therefore,the orthogonality between subcarriers of OFDMA system within a symbol will be damaged due to channel changes,which results in carrier-to-carrier Interference(ICI)reduces system performance.To meet the needs for higher-speed HSR communication quality in the future,it is necessary to rely on the fast and stable time-varying channel estimation method to further eliminate the influence of the Doppler frequency shift on the transmission signal.In the future,higher-speed HSR scenarios will have fast time-varying channels due to their relatively complex,changeable geographic environment and high moving speed.These characteristics make traditional channel estimation methods unsuitable.This thesis aims to reduce the complexity and improve the accuracy of channel estimation.Based on the existing time-varying channel estimation methods,this thesis studies more efficient time-varying channel estimation methods in future higher-speed HSR scenarios.The main content and innovations are as follows:(1)Based on the sparse characteristics of the channel in the high-speed railway environment,a generalized complex exponential model(GCE-BEM)time-varying channel estimation method that combines location information is proposed.The method first uses GCE-BEM to model the channel,and replaces the estimation of the channel with the estimation of the basis coefficients,thereby reducing the computational complexity.It is also deduced that GCE-BEM modeling can reduce intercarrier interference.Sparseness,the position information of the train is used to determine the main basis coefficients,and the channel information of all carriers on the OFDMA symbol is obtained only by estimating the main basis coefficients,which can further reduce the computational complexity and the interference between adjacent subchannels,and improve the channel estimate accuracy.Theoretical analysis and simulation results show that the proposed method can significantly improve the accuracy of channel estimation and has low computational complexity.(2)Aiming at the Multiple-Output Orthogonal Frequency Division Multiplexing Access(MIMOOFDMA)system of high-speed railway,a soft Kalman filtering iterative time-varying channel estimation method based on historical information is proposed.Considering that the channels experienced by different trains at the same location in the HSR environment with a strong correlation,the method first uses the channel information of the historical train to obtain the optimal basis function and uses it to model the channel,which reduces the computational complexity of the channel estimation and improves the accuracy of the channel estimation.Secondly,the method uses a combination of soft Kalman filtering and data detection for the estimation of the basis coefficients in each iteration.To further reduce the impact of data detection error propagation,a likelihood ratio information method is used to obtain the detection data with higher accuracy,and the detection error is processed as noise in each iteration.In addition,the soft Kalman filter used in the method does not involve the AR model tracking factor,thereby avoiding the computational complexity introduced by estimating the tracking factor.Simulation results show that the method has better estimation performance than the existing methods,and is more suitable for time-varying channel acquisition in actual high-speed mobile scenarios.