A Semi-blind Method for MIMO Channel Identification

A semi-blind algorithm to perform channel identification for multiple input multiple output(MIMO) systems in frequency and time-selective fading environments is proposed. It contains two steps:training and tracking.In training phase, improved Kalman filtering,namely robust Kalman filtering (RKF), is exploited to identify channel impulse response(CIR). After that.in tracking stage,the RKF and minimum mean-square error feedback decision equalizer (MMSE-DFE) cooperate to track the time-varying channel.The RKF recursions is presented and a closed-form solution for baud rate MIMO MMSE-DFE under perfect knowledge of CIR and correct past decisions conditions is derived. In addition, it regards unknown dc-offset due to zero intermediate frequency(IF) at the receiver as the mean of measurement noise, which is estimated as a byproduct through robust Kalman filters. Finally, it is compared with well- known ones, such as least mean-square(LMS), recursive least square (RLS),Kalman filtering (KF). All these show that the proposal exhibits better performance.