Noise-predictive decision-feedback detection for multiple-input multiple-output channels

The decision-feedback (DF) detector is a nonlinear detection strategy for multiple-input multiple-output (MIMO) channels that can significantly outperform a linear detector, especially when the order in which the inputs are detected is optimized according, to the so-called Bell Labs Layered Space-lime (BLAST) ordering. The DF detector may be implemented as the cascade of a linear detector, which mitigates interference at the expense of correlating the noise, followed by a noise predictor, which exploits the correlation in the noise to reduce its variance. With this architecture, existing linear detectors can be easily upgraded to DF detectors. We propose a low-complexity algorithm for determining the BLAST ordering that is facilitated by the noise-predictive architecture. The resulting ordered noise-predictive DF detector requires fewer computations than previously reported ordered-DF algorithms. We also propose and derive the ordered noise-predictive minimum-mean-squared-error DF detector and show how to determine its BLAST ordering with low complexity.