Estimation of space-time varying channels using signal subspace projection and soft information

We consider the uplink of a time-slotted wireless communication system with a mobile user and a base station equipped with an antenna array of M elements. The space-time varying channel of the system is usually estimated using training sequence in each slot and refined by projecting it to the corresponding spatial signal subspace. However, this method does not work when the space-time channel matrix is full row rank. In this paper, we formulate the channel by a Sylvester matrix using the space-time signal model for this full row rank case and propose a channel estimation method based on the space-time signal subspace projection. We also propose a channel estimation method that incorporates the extended training symbols by the soft information fed back from the decoder to improve the channel estimation performance. Numerical results show that these two methods can be performed separately or jointly to improve the performance of the space-time channel estimation by training sequences.