Approaching Optimal Performance By Lattice-Reduction Aided Soft Detectors

Lattice reduction (LR) technique has been introduced into the process of linear equalization to improve the performance. It has been shown that LR-aided hard detectors collect full diversity with low complexity for many transmission systems. However, though LR-aided linear equalizers collect the same diversity as that collected by the maximum-likelihood (ML) detector, there still exists a performance gap between LR-aided and ML equalizers. To fill this gap, one may use soft detectors. In this paper, we give two LR-aided soft detectors with different candidates generation methods. We compare the performance and complexity of our algorithms with the existing alternatives and show that our methods can achieve near-optimal performance. The performance-complexity tradeoff of our proposed algorithms is also studied. Simulation results validate the effectiveness of our algorithms.