The sorted-QR Chase detector for multiple-input multiple-output channels

The performance of a decision-feedback detector on a fading multiple-input multiple-output channel is limited by the low diversity of the first symbol detected. We propose a new family of detection techniques which overcomes this bottleneck by using a list detector for the first symbol; the list detector is then combined with a parallel bank of decision-feedback detectors, one for each element of the list. The detector family is parameterized by the length of the list, which can be adjusted to achieve a wide range of attractive trade-offs between performance and complexity. For example, on a 4-input 4-output Rayleigh-fading channel with uncoded 16-QAM inputs, one version of the proposed detector outperforms the popular minimum-mean-squared-error BLAST-ordered decision-feedback detector by 1.5 dB, while simultaneously requiring 6% fewer computations.