Statistical Precoding With Decision Feedback Equalization Over a Correlated MIMO Channel

The decision feedback (DF) transceiver, combining linear precoding and DF equalization, can establish point-to-point communication over a wireless multiple-input multiple-output channel. Matching the DF-transceiver design parameters to the channel characteristics can improve system performance, but requires channel knowledge. We consider the fast-fading channel scenario, with a receiver capable of tracking the channel-state variations accurately, while the transmitter only has long-term, channel-distribution information. The receiver design problem given channel-state information is well studied in the literature. We focus on transmitter optimization, which amounts to designing a statistical precoder to assist the channel-tailored DF equalizer. We develop a design framework that encompasses a wide range of performance metrics. Common cost functions for precoder optimization are analyzed, thereby identifying a structure of typical cost functions. Transmitter design is approached for typical cost functions in general, and we derive a precoder design formulation as a convex optimization problem. Two important subclasses of cost functions are considered in more detail. First, we explore a symmetry of DF transceivers with a uniform subchannel rate allocation, and derive a simplified convex optimization problem, which can be efficiently solved even as system dimensions grow. Second, we explore the tractability of a certain class of mean square error based cost functions, and solve the transmitter design problem with a simple algorithm that identifies the convex hull of a set of points in
$\BBR^{2}$
. The behavior of DF transceivers with optimal precoders is investigated by numerical means.