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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.

The Decision Feedback Equalizer (DFE) is a known method of channel equalization which has performance superiority over linear equalizer. The best performance of DFE is obtained, commonly, with training period which is used for initial acquisiton of channel or recovering changes in the channel. The training period requires a training sequence which reduces the bit transmission rate or is not possible to send in most of the situations. So, it is desirable to skip the training period. The Unsupervised (Blind) DFE (UDFE) is such a DFE scheme which has no training period. The UDFE has two modes of operation. In one mode, the UDFE uses Constant Modulus Algorithm (CMA) to perform channel acquisition, blindly. The other mode is the same as classical decision-directed DFE. This thesis compares the performances of the classical trained DFE method and the UDFE. The performance comparison is done in some channel environments with the problem of timing error present in the received data bearing signal. The computer aided simulations are done for two stationary channels, a time-varying channel and a frequency selective Rayleigh fading channel to test the performance of the relevant equalizers. The test results are evaluted according to mean square error (MSE), bit-error rate (BER), residual intersymbol interference (RISI) performances and equalizer output diagrams. The test results show that the UDFE has an equal or, sometimes, better performance compared to the trained DFE methods. The two modes of UDFE enable it to solve the absence of training sequence.