Lattice Reduction aided Selective Spanning with Fast Enumeration for soft-output MIMO detection

Lattice Reduction (LR) is a promising technique to improve the performance of linear MIMO detectors. However, LR-aided linear hard output MIMO detection is still far from optimal. Practical systems use soft output information to exploit gains from forward-error-correcting codes to achieve near-optimal performance. In this paper, LR-aided Selective Spanning with Fast Enumeration (LR-SSFE) is proposed as a candidate list generation method for soft output MIMO detection. The proposed algorithm uses heuristics based on simple arithmetic operations, which results in a completely deterministic and regular data flow. Hence, LR-SSFE can be efficiently implemented on a parallel programmable architecture. LR-SSFE is compared to the Fixed Candidates Algorithm (FCA) in terms of performance and complexity, which is another LR-aided candidate list generation method. Under the same performance constraints LR-SSFE has a significantly lower complexity than FCA.