In this paper, we deal with low-complexity near-optimal
detection/equalization in large-dimension multiple-input multiple-output
inter-symbol interference (MIMO-ISI) channels using message passing on
graphical models. A key contribution in the paper is the demonstration that
near-optimal performance in MIMO-ISI channels with large dimensions can be
achieved at low complexities through simple yet effective
simplifications/approximations, although the graphical models that represent
MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1)
use of Markov Random Field (MRF) based graphical model with pairwise
interaction, in conjunction with {\em message/belief damping}, and 2) use of
Factor Graph (FG) based graphical model with {\em Gaussian approximation of
interference} (GAI). The per-symbol complexities are O(K2nt2β) and
O(Kntβ) for the MRF and the FG with GAI approaches, respectively, where K
and ntβ denote the number of channel uses per frame, and number of transmit
antennas, respectively. These low-complexities are quite attractive for large
dimensions, i.e., for large Kntβ. From a performance perspective, these
algorithms are even more interesting in large-dimensions since they achieve
increasingly closer to optimum detection performance for increasing Kntβ.
Also, we show that these message passing algorithms can be used in an iterative
manner with local neighborhood search algorithms to improve the
reliability/performance of M-QAM symbol detection