Iterative detection based on reduced-rank equalization

In this paper, we consider an iterative or turbo receiver with linear detection using the Wiener Filter (WF), i.e. the optimal linear filter based on the Mean Square Error (MSE) criterion. Multiple antennas at the receiver increase the dimension of the observation vector which results in computational intense detectors. We extend an optimal hut computational efficient algorithm, originally derived for a single receive antenna, to Single-Input Multiple-Output (SIMO) channels. To further reduce computational complexity, we apply the suboptimal low-rank Multi-Stage Wiener Fitter (MSWF) and approximate additionally second order statistics of non-stationary random processes by their time-invariant averages. Complexity investigations reveal the enormous capability of the proposed algorithms to decrease computational effort. Moreover, simulation results show that the reduced-rank MSWF behave near optimum although the rank is drastically reduced to two or even one.