Optimal training sequence design for MIMO-OFDM in spatially correlated fading environments

Abstract

Multiple Input Multiple Output with Orthogonal Frequency Division Multiplexing (MIMOOFDM) has been widely adopted as one of the most promising air interface solutions for future broadband wireless communication systems due to its high rate transmission capability and robustness against multipath fading. However, these MIMO-OFDM advantages cannot be achieved unless the channel state information (CSI) can be obtained accurately and promptly at the receiver to assist coherent detection of data symbols. Channel estimation and training sequence design are, therefore, still open challenges of great interest.

In this work, we investigate the Linear Minimum Mean Square Error (LMMSE) channel estimation and design nearly optimal training sequences for MIMO-OFDM systems in spatially correlated fading. We, first, review the LMMSE channel estimation model for MIMO-OFDM in spatially correlated fading channels. We, then, derive a tight theoretical lower bound of the channel estimation Mean Square Error (MSE). By exploiting the information on channel correlation matrices which is available at the transmitter, we design a practical and nearly optimal training sequence for MIMO-OFDM systems . The optimal transmit power allocation for training sequences is found using the Iterative Bisection Procedure (IBP). We also propose an approximate transmit power allocation algorithm which is computationally more efficient than the IBP while maintaining a similar MSE performance. The proposed training sequence design method is also applied to MIMO-OFDM systems where Cyclic Prefixing OFDM (CP-OFDM) is replaced by Zero Padding OFDM - OverLap-Add method (ZP-OFDM-OLA). The simulation results show that the performance of the proposed training sequence is superior to that of all existing training sequences and almost achieves the MSE theoretical lower bound.