| Summary: | 博士 === 國立交通大學 === 電信工程系所 === 97 === Multiple-input multiple-output (MIMO) technology has been included in many industrial standards to achieve significant throughput enhancement compared with conventional single antenna systems. By using multi-element antennas at both transmit and receive sides, multiple data streams can be transmitted simultaneously through parallel spatial modes. To realize the advantages of MIMO systems, accurate channel state information (CSI) is indispensable, especially for high rate transmissions. With the increase of antenna number, the task of estimating or processing a MIMO channel matrix becomes more and more difficult. In this thesis, we propose an efficient channel representation such that the number of required parameters is reduced and the computation complexity can be lessened as well. For medially to highly correlated MIMO environments, the proposed representation can lead to significant parametric dimension reduction while maintaining good CSI quality.
Based on the proposed channel representation, we develop iterative least squared (LS) schemes to estimate several typical MIMO channels. The reduced-rank CSI representation is very useful for many post-channel-estimation operations that require processing the instantaneous channel matrices. Depending on the specified modelling order, the proposed channel estimators offer tradeoff between identification accuracy and computational complexity. Moreover, the dimension-reduction induced noise rejection effect enables the proposed model-based estimators to achieve superior mean squared error (MSE) performance over certain SNR region when compared with that of the conventional LS approach. Theoretical analysis and numerical simulations of MSE performance are provided to assess the estimators’ performance and validate the analytical predictions.
Taking advantage of the proposed compact CSI representation, we proceed to develop a model-based feedback precoded system. By incorporating our new channel representation into the precoder design, the resulting precoded system provides significant reductions on the feedback bandwidth and the computational complexity needed for constructing the precoder and equalizer matrices. Numerical results show that compared with the conventional approaches that need full knowledge of instantaneous CSI, our proposal suffers only negligible performance degradation at very high SNR region. The reductions on computing complexity and feedback channel bandwidth, nevertheless, are significant. To assess the performance of our model-based approach, we establish several bounds regarding the reception error and feedback information loss. Simulated results are compared with these analytical bounds to verify that performance trends can indeed be accurate predicted.
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