MIMO channel identification using joint white noise statistics estimation and Kalman filtering

提出了一种新的时间选择性衰落环境下MIMO信道辨识算法。为了提高信息传输效率,训练序列被直接叠加于信息序列之上。算法将信息符号输出、接收端AWGN和由于采用零中频接收技术而产生的直流偏移当做虚拟的观测噪声,其均值和自协方差均未知。通过联合的递推白噪声统计估计器和卡尔曼滤波器对时变信道进行跟踪,推导了一种计算简单的次优无偏时变白噪声统计估计器。以简单有效的方法抑制直流偏移对辨识精度的影响。仿真结果表明了算法具有良好的性能。A novel scheme to perform multiple input multiple output (MIMO) channel identification in time -selective fading environments is suggested. In order to improve transmission efficiency, training sequences are arithmetically added to the information symbols. The output of information symbols ,additive white noises and dc -offsets at the receiver are regarded as fiction measurement noise, whose mean and autocovariance are both unknown.A time -varying measurement noise recursive estimator and Kalman filters cooperate to track the time - varying MIMO channel impulse response (CIR).A low complexity,sub -optimal and unbiased time-varying white noise statistics estimator is derived. Moreover, the influence of dc-offset is restrained in a simple way. Finally,it is compared with some existing methods, and all these indicate that the proposal exhibits good performance.国家高技术研究发展(863计划)项目(2006AA09Z108);; 国家自然科学基金资助项目(05C777