Blind Multidimensional Matched Filtering for Wireless Channels with Multiple Antennas

The hardware complexity is one of the most crippling handicaps of implementing a blind channel estimation scheme in a wireless receiver. The state-of-the-art blind channel estimation methods either need matrix decomposition methods, which are implementation inefficient and expensive, or higher order cumulant functions of the received data, which eventually leads to the need for long data records for accurate estimation of higher order statistics of the received signal. The Blind Matched Filter (BMF) receiver on the other hand makes use of an adaptive algorithm, i.e. the Constant Modulus Algorithm (CMA), to estimate the channel without the need for an implementation inefficient approach as stated above. The BMF method assumes that the receiver utilizes channel matched filtering before the equalization of the channel. For receivers, employing multiple antennas, it is well known that the optimal detection strategy is to pass the received signals through a multidimensional matched filter. For this reason a signal transmission strategy similar to that of the BMF receiver would definitely benefit communication systems employing multiple antennas. In this thesis four novel approaches have been proposed, all implementing multidimensional matched filtering blindly in their architecture, for wireless communication channels with multiple antennas. Two of these approaches are for channels utilizing multiple antennas only for signal reception. These two approaches differ from each other in the channel equalizer structure that they employ. The first approach is the blind implementation of the correlation-based Decision Feedback Equalizer (DFE). The second approach equalizes the Single Input Multiple Output (SIMO) channel and obtains its estimate by making use of the multichannel DFE. The multidimensional