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Low-complexity near-optimal signal detection in large dimensionalcommunication systems is a challenge. In this paper, we present a reactive tabusearch (RTS) algorithm, a heuristic based combinatorial optimization technique,to achieve low-complexity near-maximum likelihood (ML) signal detection inlinear vector channels with large dimensions. Two practically importantlarge-dimension linear vector channels are considered: i) multiple-inputmultiple-output (MIMO) channels with large number (tens) of transmit andreceive 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 ofincreased spectral efficiency (several tens of bps/Hz) and the latter offersthe benefit of high time-diversity orders. Our simulation results show that,while algorithms including variants of sphere decoding do not scale well forlarge dimensions, the proposed RTS algorithm scales well for signal detectionin large dimensions while achieving increasingly closer to ML performance forincreasing number of dimensions.

Near-ML Signal Detection in Large-Dimension Linear Vector Channels Using Reactive Tabu Search