On The Fast Time-varying Channel Modeling And The Channel Estimation For MIMO OFDM Systems

High data rate and reliable data transmission are highly desirable in the future wireless communication systems over the high mobility environment. In MIMO OFDM systems, higher data rate implies larger bandwidth and smaller sampling interval. While the small interval less than the channel delay spread will lead to frequency selective fading and the ISI presents. In addition, the Doppler spread incurred by the relative mobility between transmitter and receiver causes time varying fading and the ICI for OFDM. Thus, it is imperative for receiver to obtain accurate channel estimate for the subsequent processing like the channel euqualization over doubly selective fading channel.Based on the analysis of doubly selective fading channel characteristics in MIMO OFDM systems, each time-varying channel tap within one OFDM symbol can be approximated by a Basis Expansion Model (BEM), which can reduce the number of channel parameters to a great extent. And the approximation accuracy of using different BEM models are presented for comparison. Based on the BEM model, the channel estimation can be realized by employing Kalman filter estimator or the Maximum Likelyhood (ML) one. On the other hand, considering the inherent sparsity of the doubly selective fading channel, the sparse representation of fast fading channel over the Delay-Doppler domain is addressed to incorporate the compressive sensing theory into the channel state information estimate by using the reconstruction algorithm, such as the traditional Othogonal Matching Pursuit algorithm. Since the compressive sensing based channel estimate quality is highly dependent on the channel sparsity degree, an improved algorithm, which is independent of the involved channel, is proposed to ensure the stability and good estimate performance.Duo to the impact of carrier frequency offset (CFO) in practical system, joint time-varying channel and CFO estimate in the presence of CFO is considered. Based on the BEM based MIMO OFDM system model, the extended Kalman filter estimator can be employed to jointly derive the CFO and the channel. Another method is the Expectation Maximization (EM) algorithm. A novel joint doubly-selective channel and CFO estimation scheme is proposed based on the EM algorithm for MIMO OFDM system. It is shown that the proposed algorithm the proposed estimate scheme can achieve reasonable estimate performance with low complexity, in particular a ML based coarse CFO estimation and the modified Q function utilized in the E-step of the proposed EM algorithm.