Reduced Rank Channel Estimation

A space-time wireless communication channels can be decomposed as a set of filters, each consisting of a scalar temporal filter followed by a single spatial signature vector. If only a small number of such filters is necessary to accurately describe the space time channel, we call it a reduced rank channel. We here consider different methods of exploiting this property to improve channel estimation and subsequent space-time equalization performance. Three methods have been studied, a maximum likelihood reduced rank channel estimation method and two different signal subspace projection methods which projects either the channel estimate or the received data samples onto an estimate of the signal subspace, the latter being the new method proposed here. Simulations indicate that even though the maximum likelihood reduced rank method has the smallest channel estimation errors, the BER of the detector based on this model exceeds the BER of the detectors based on the channel models obtained using the two signal subspace projection methods. The best performance is obtained using the proposed method, which also has the lowest complexity.