  Low-complexity near-optimal signal detection in large dimensional
communication systems is a challenge. In this paper, we present a reactive tabu
search (RTS) algorithm, a heuristic based combinatorial optimization technique,
to achieve low-complexity near-maximum likelihood (ML) signal detection in
linear vector channels with large dimensions. Two practically important
large-dimension linear vector channels are considered: i) multiple-input
multiple-output (MIMO) channels with large number (tens) of transmit and
receive antennas, and ii) severely delay-spread MIMO inter-symbol interference
(ISI) channels with large number (tens to hundreds) of multipath components.
These channels are of interest because the former offers the benefit of
increased spectral efficiency (several tens of bps/Hz) and the latter offers
the benefit of high time-diversity orders. Our simulation results show that,
while algorithms including variants of sphere decoding do not scale well for
large dimensions, the proposed RTS algorithm scales well for signal detection
in large dimensions while achieving increasingly closer to ML performance for
increasing number of dimensions.
