Joint MIMO Channel Estimation and Equalization in Doubly-Selective Fading Environments

A improved algorithm to perform channel estimation and equalization for multiple input multiple output(MIMO) systems in frequency and time-selective fading environments is put forward. lt contains two steps: training and tracking. In training phase, improved Kalman filtering, namely robust Kalman filtering(RKF), is exploited to estimate 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.