Efficient channel estimation for block transmission systems
Block transmission systems have recently gained considerable interest as a promising method for high data rate communications. This is due to their uncomplicated implementation and simple equalization of frequency-selective fading channels. For coherent signal detection and channel equalization in block transmission systems, channel state information (CSI) should be known to, or estimated at, the receiver. In this dissertation, we present three approaches for efficient channel estimation in block transmission systems. First, to provide a bandwidth-efficient solution for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel estimation, we establish conditions for channel identifiability and propose a blind channel estimation method based on a subspace technique. Second, to relax existing strict conditions for blind MIMO channel identification without a sacrifice of data rates and to provide a bandwidth-efficient solution for channel estimation in MIMO block transmission systems with a cyclic prefix, we present a framework for blind channel estimation based on a general non-redundant precoding. Using this framework, we propose a blind channel estimator exploiting a simplified non-redundant precoding. To complete the channel estimation, we also develop a technique for resolving the channel ambiguity in the proposed method. Third, in rapid mobile environments where channels change very fast, blind channel estimation techniques may not be suitable to obtain CSI due to their relatively slow convergence. In this case, to achieve accurate estimation of doubly selective channels in OFDM systems, we propose an optimal (in the sense of mean square error) pilot tone placement applicable to OFDM systems regardless of the time variations of a channel. In addition, we present an accurate linear minimum mean square error (LMMSE) channel estimator that exploits a small number of pilot tones located according to the derived optimal placement. To achieve computationally efficient channel estimation with lower complexity than the LMMSE estimator and to obtain performance close to the LMMSE estimator, an approximate LMMSE (ALMMSE) channel estimator is also proposed. Finally, we propose a novel iterative ALMMSE channel estimator that achieves better performance than the LMMSE and ALMMSE estimators, while having complexity in between the two.