Channel Estimation in Wireless Communication Systems Employing Multiple Antennas
Antennas that are able to adaptively direct the transmitted (and received) energy are of great interest in future wireless communication systems. The directivity implies reduced transmit power and interference, and also a potential for increased capacity. The focus of this thesis is on channel estimation in wireless communication systems that employ multiple antennas. The thesis is divided into two parts, whereof the first part addresses the problem of parameter estimation of a distributed source. Due to, e.g., local scattering around the transmitter, the source, as seen from the receiver, appears spatially distributed. A characterization of the spatial channel, in particular mean direction of arrival and spatial spread, is of interest for system optimization and performance prediction. Low-complexity beamforming-based estimators are introduced for the estimation of direction and spatial spread of the distributed source. Despite not exploiting the model, the proposed non-parametric estimators are found to show very competitive performance compared to more complex parametric methods. Also provided is a statistical analysis of their performance. The second part of the thesis deals with Multiple-Input Multiple-Output (MIMO) channel estimation. A simple and straightforward way of estimating the unknown channel is to transmit known training/pilot sequences. In the recent past, a number of publications have suggested Superimposed Pilots (SIP) for channel estimation in MIMO systems. However, the performance gain achieved by SIP compared to conventional (time-multiplexed) training is still questionable. To evaluate the performance of the various training-based schemes, a lower bound on the ergodic channel capacity of a general training-based scheme applied to a block-wise flat-fading MIMO channel is derived and evaluated. It is found that in certain scenarios (many receive antennas and short channel coherence times), it is beneficial to also transmit data during the training mode (i.e. use SIP). The main conclusion though, is that the general SIP-scheme quite often reduces to the conventional time-multiplexed scheme, and, hence, renders the same capacity.