Reduced rank channel estimation

A space-time wireless communication channel can be decomposed into 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 consider different methods of exploiting this property to improve the channel estimation and subsequent space-time equalization. Three methods have been studied, a maximum likelihood reduced rank channel estimation method and two different signal subspace projection methods. The first method projects the channel estimate onto an estimate of the signal subspace. The second, which is the new method proposed here, projects the received data onto the same estimate of the signal subspace. 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 either of the signal subspace projection methods. The best performance is obtained using the proposed method, which also has the lowest complexity