MIMO-OFDM系统中基于变分Bayes EM算法的联合符号检测与鲁棒Kalman信道跟踪

Joint symbol detection and robust kalman channel tracking for MIMO-OFDM systems via the variational bayes EM algorithm

基于变分Bayes期望最大化VBEM(variational Bsayes expectation maximization)算法和Turbo原理,提出了快时变信道条件下MIMO-OFDM系统中的联合符号检测与信道估计算法.在VBEM框架下,信号检测和信道估计分别由修正的列表球形译码算法和软输入Kalman算法完成,检测器和估计器分别考虑了信道和检测信号的估计误差协方差矩阵.当信道时变剧烈时,存在较大检测误差的数据在软输入Kalman算法中引入异常值(outliers),由于Kalman算法对于异常值的敏感性,系统会在错误传播的影响下出现误码平台.为削弱异常值的影响,利用鲁棒统计理论设计了VBEM框架下改进的鲁棒软输入Kalman算法,该算法能在出现异常值的条件下保持较好的信道跟踪能力.仿真结果表明:在快速时变多径信道条件下,文中设计的鲁棒VBEM算法优于传统的VBEM算法和EM算法.

A new joint symbol detection and channel estimation algorithm is proposed for MIMO-OFDM systems over fast fading channels based on the Variational Bayes Expectation-Maximization （VBEM） algorithm and turbo principle. By applying VBEM algorithm, a modified List Sphere Decoder （LSD） and soft-input Kalman Filter （KF） are derived for symbol detection and channel estimation, respectively. The covariance matrices of channel estimation are considered in the symbol detector, and the covariances of symbols are taken into account in channel estimator. In fast time-varying channels, soft symbols with low reliability may introduce outliers in soft-input kalman filter. Since the KF lacks robustness to outliers, filter divergence may occur and gives rise to error propagation and ＂error floor＂ phenomenon. In order to mitigate the outliers＇ effect, a robust soft-input kalman filter under the VBEM framework is proposed based on the theory of robust statistics, which can keep robust channel-tracking ability in fast fading channels. Simulation results show that the proposed VBEM algorithm has better performance over the conventional VBEM algorithm and EM algorithm in fast time-varying multipath channels.