  Linear data-detection algorithms that build on zero forcing (ZF) or linear
minimum mean-square error (L-MMSE) equalization achieve near-optimal spectral
efficiency in massive multi-user multiple-input multiple-output (MU-MIMO)
systems. Such algorithms, however, typically rely on centralized processing at
the base-station (BS) which results in (i) excessive interconnect and chip
input/output (I/O) data rates and (ii) high computational complexity.
Decentralized baseband processing (DBP) partitions the BS antenna array into
independent clusters that are associated with separate radio-frequency
circuitry and computing fabrics in order to overcome the limitations of
centralized processing. In this paper, we investigate decentralized
equalization with feedforward architectures that minimize the latency
bottlenecks of existing DBP solutions. We propose two distinct architectures
with different interconnect and I/O bandwidth requirements that fuse the local
equalization results of each cluster in a feedforward network. For both
architectures, we consider maximum ratio combining, ZF, L-MMSE, and a nonlinear
equalization algorithm that relies on approximate message passing, and we
analyze the associated post-equalization signal-to-noise-and-interference-ratio
(SINR). We provide reference implementation results on a multi graphics
processing unit (GPU) system which demonstrate that decentralized equalization
with feedforward architectures enables throughputs in the Gb/s regime and
incurs no or only a small performance loss compared to centralized solutions.
