  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 all
symbol candidates has exponential complexity and may not be applicable in many
practical systems. In this paper, we consider a generalized minimum mean square
error (MMSE) detection derived from conditional mean estimation, which in
principle behaves equivalently to MLD but also includes a linear MMSE detection
as a special case. Motivated by this fact, we propose a low-complexity
detection which significantly reduces the number of examined symbol candidates
without significant error rate performance degradation from MLD. Our approach
is to approximate the probability density function (pdf) of modulated symbols
that appears in the exact conditional mean expression such that the decision
metric can be cast into a partially closed form. It is found that uniform ring
approximation in combination with phase shift keying (PSK) and amplitude phase
shift keying (APSK) is promising, as it can achieve a performance even
comparable to MLD, while its complexity is linear when the number of transmit
antennas is two.
