Research On Channel Estimation And Equalization For Single Carrier Systems Over Doubly Selective Fading Channels Of XIDIAN UNIVERSITY

In high-speed wideband wireless communication systems,due to the reduced symbol interval,several paths of signals with different path delays can be classified at the receiver,which caused multipath effects and introduced frequency selective fading.The channel is not time-varying.However,once each of the communication partners is in high-speed motion,or the surrounding environment changes rapidly,the resulting Doppler shift(fd)will introduce the time-selectivity,thus forming a time-varying and frequency selective fading channel,referred to as doubly selective fading channels.In our daily life,such as the highspeed cars,trains or ships on rough sea are typical sites for doubly selective fading channels.And the communication signal will encounter more severe fading and interference than the static multipath channels.Therefore,the channel estimation and equalization schemes have attracted widespread attention to mitigate these influences.This paper aims to investigate the channel estimation and equalization algorithms for single carrier systems over doubly selective fading channels,and the main research and contributions are concluded as follows:1.Considering the classical exponentially weighted recursive least square(EW-RLS)channel estimation algorithm,with complicate solutions to obtain gain matrix and estimation error variance matrix,this paper proposes two kinds of low-complexity estimation algorithms.The square-root Kalman filtering(SR-KF)algorithm and the QR decomposition-based EW-RLS(QR-EW-RLS)algorithm.By decomposing formulas,constructing matrices and applying SR-KF filtering and QR decomposition respectively,the two key matrices are obtained in only one step.Finally,both the algorithms reduce the calculation complexity by a factor of level and maintain the excellent performance of EW-RLS.2.Based on the turbo equalization scheme,the extended Kalman filtering(EKF),which jointly performs channel estimation and equalization,this paper proposes a linear Kalman filtering(LKF)algorithm.The channel estimation and equalization are separated in the proposed LKF.By applying the QR-EW-RLS channel estimation algorithm,the assumption that the channel follows the first-order autoregressive variation is abandoned,resulting more accurate estimation results.Thus the equalization performance is improved.Furthermore,the proposed LKF outperforms the EKF in bit error rate(BER)performance and reduces the calculation complexity as the length of state space vector is shorter than EKF.3.Based on the non-oversampled single carrier system,where the proposed LKF operates in,this paper proposes a linear time-domain turbo equalization scheme for integrally oversampled single carrier systems(OSC-LKF).The key procedure of this algorithm is to investigate the measurement equation,which is the relationship between the state space vector and the received signals,by constructing three key matrices using the wave forming filter,the channel state information and the matched filter.Finally,the proposed OSC-LKF improves the BER dramatically compared with LKF algorithm.Finally,the comparisons of calculation complexity and BER performance are presented and the validity of the proposed schemes are illustrated by simulation results.