Abstract
In this paper, a receiver structure which combines multiuser detection (temporal filtering) and receiver beamforming (spatial filtering) in a multipath environment is considered. Following Yener et al. [2001] and Miller and Schwartz [1995], we model the receiver as a linear matrix filter and use the minimum mean-squared error (MMSE) as the performance criterion. Motivated by the complexity of the optimum receiver, we propose rank constrained temporal-spatial filters which are simpler and near optimum. The MSE is minimized subject to a structural constraint, using an iterative alternating minimization algorithm. Numerical results indicate that a performance close to that of the optimum filter can be achieved with a simple iterative structure, even in highly loaded systems. Least mean squares (LMS) is used to formulate the adaptive implementations and the convergence properties are investigated along with the effect of the number of multipath components.
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