  In this paper, we study the low-complexity iterative soft-input soft-output
(SISO) detection algorithm in a large-scale distributed multiple-input
multiple-output (MIMO) system. The uplink interference suppression matrix is
designed to decompose the received multi-user signal into independent
single-user receptions. An improved minimum-mean-square-error iterative soft
decision interference cancellation (MMSE-ISDIC) based on eigenvalue
decomposition (EVD-MMSE-ISDIC) is proposed to perform low-complexity detection
of the decomposed signals. Furthermore, two iteration schemes are given to
improve receiving performance, which are iterative detection and decoding (IDD)
scheme and iterative detection (ID) scheme. While IDD utilizes the external
information generated by the decoder for iterative detection, the output
information of the detector is directly exploited with ID. In particular, a
large-scale distributed MIMO prototyping system is introduced and a 32$ \times
$32 (4 user equipments (UEs) and 4 remote antenna units (RAUs), each equipped
with 8 antennas) experimental tests at 3.5 GHz was performed. The experimental
results show that the proposed iterative receiver greatly outperforms the
linear MMSE receiver, since it reduces the average number of error blocks of
the system significantly.
