Approximate Maximum Likelihood Detectors for MIMO Spatial Multiplexing Systems

In multiple antenna systems using spatial multiplexing to raise data rates, it is preferable to use maximum likelihood (ML) detection to fully benefit from multiplexing and diversity gain. In this paper, we present two tree based near ML detectors that use new ordering criteria in conjunction with an efficient tree search strategy. Compared with conventional tree detectors, the error performance of the new detectors closely approximates that of the exact ML detector while achieving a dramatic reduction in complexity. Moreover, the new schemes ensure a fixed detection delay and parallelization in tree search.