Lattice Reduction-aided Minimum Mean Square Error K-Best detection for MIMO systems

The Multiple-Input Multiple-Output (MIMO) with a Spatial-Multiplexing scheme is a topic of high interest for the next generation of wireless communications systems. In this paper, we propose to approach the Maximum Likelihood (ML) performance through the combination of a neighbourhood study and a Lattice Reduction (LR)-aided solution. Moreover, by introducing a neighbourhood study in the reduced domain, we propose in this paper a novel equivalent metric that is based on the combination of the LR-aided Minimum-Mean Square Error solution. We show that the proposed metric presents a relevant complexity reduction while maintaining near-ML performance. In particular, the corresponding computational complexity is polynomial in the number of antennas while it is shown to be independent of the constellation size. For a 4×4 MIMO system with 16-QAM modulation on each layer, the proposed solution is simultaneously near-ML and ten times less complex than the classical neighbourhood-based K-Best solution.