  The fundamental problem of our interest here is soft-input soft-output
multiple-input multiple-output (MIMO) detection. We propose a method, referred
to as subspace marginalization with interference suppression (SUMIS), that
yields unprecedented performance at low and fixed (deterministic) complexity.
Our method provides a well-defined tradeoff between computational complexity
and performance. Apart from an initial sorting step consisting of selecting
channel-matrix columns, the algorithm involves no searching nor algorithmic
branching; hence the algorithm has a completely predictable run-time and allows
for a highly parallel implementation. We numerically assess the performance of
SUMIS in different practical settings: full/partial channel state information,
sequential/iterative decoding, and low/high rate outer codes. We also comment
on how the SUMIS method performs in systems with a large number of transmit
antennas.
