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In multiple-input multiple-output (MIMO) spatially multiplexing (SM) systems,achievable error rate performance is determined by signal detection strategy.The optimal maximum-likelihood detection (MLD) that exhaustively examines allsymbol candidates has exponential complexity and may not be applicable in manypractical systems. In this paper, we consider a generalized minimum mean squareerror (MMSE) detection derived from conditional mean estimation, which inprinciple behaves equivalently to MLD but also includes a linear MMSE detectionas a special case. Motivated by this fact, we propose a low-complexitydetection which significantly reduces the number of examined symbol candidateswithout significant error rate performance degradation from MLD. Our approachis to approximate the probability density function (pdf) of modulated symbolsthat appears in the exact conditional mean expression such that the decisionmetric can be cast into a partially closed form. It is found that uniform ringapproximation in combination with phase shift keying (PSK) and amplitude phaseshift keying (APSK) is promising, as it can achieve a performance evencomparable to MLD, while its complexity is linear when the number of transmitantennas is two.

A Generalized MMSE Detection with Reduced Complexity for Spatially Multiplexed MIMO Signals