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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.
Based on Variational Bayes Expectation Maximization (VBEM) algorithm and Turbo principle, a joint symbol detection and channel estimation algorithm is proposed in MIMO-OFDM system under fast time-varying channel. Under the framework of VBEM, signal detection and channel The estimation is completed by a modified list sphere decoding algorithm and a soft-input Kalman algorithm, respectively, and the detector and estimator respectively consider the estimated error covariance matrix of the channel and the detection signal. When the channel becomes intense, there is data of large detection error Due to the sensitivity of Kalman algorithm to outlier, the system will appear error code platform under the influence of false propagation.In order to weaken the influence of outliers, a robust statistical theory is used to design out VBEM framework to improve the robust soft-input Kalman algorithm, the algorithm can keep better channel tracking ability under the condition of abnormal value.The simulation results show that under the condition of fast time-varying multipath channel, the robust VBEM The algorithm is superior to the traditional VBEM algorithm and EM algorithm.