  This paper presents an optimized low-complexity and high-throughput
multiple-input multiple-output (MIMO) signal detector core for detecting
spatially-multiplexed data streams. The core architecture supports various
layer configurations up to 4, while achieving near-optimal performance, as well
as configurable modulation constellations up to 256-QAM on each layer. The core
is capable of operating as a soft-input soft-output log-likelihood ratio (LLR)
MIMO detector which can be used in the context of iterative detection and
decoding. High area-efficiency is achieved via algorithmic and architectural
optimizations performed at two levels. First, distance computations and slicing
operations for an optimal 2-layer maximum a posteriori (MAP) MIMO detector are
optimized to eliminate the use of multipliers and reduce the overhead of
slicing in the presence of soft-input LLRs. We show that distances can be
easily computed using elementary addition operations, while optimal slicing is
done via efficient comparisons with soft decision boundaries, resulting in a
simple feed-forward pipelined architecture. Second, to support more layers, an
efficient channel decomposition scheme is presented that reduces the detection
of multiple layers into multiple 2-layer detection subproblems, which map onto
the 2-layer core with a slight modification using a distance accumulation stage
and a post-LLR processing stage. Various architectures are accordingly
developed to achieve a desired detection throughput and run-time
reconfigurability by time-multiplexing of one or more component cores. The
proposed core is applied as well to design an optimal multi-user MIMO detector
for LTE. The core occupies an area of 1.58MGE and achieves a throughput of 733
Mbps for 256-QAM when synthesized in 90 nm CMOS.
