Enhancing the Performance of the Quasi-ML Receiver (Detector Plus Decoder) for Coded MIMO Systems Via Statistical Information.

We propose a statistical information-based approach to enhance the performance of the noniterative quasi-maximum-likelihood (QML) receiver (detector plus decoder) recorded at the output of the channel decoder for coded multiple-input–multiple-output (MIMO) systems. As an illustrative example, the QR decomposition with an $M$-algorithm (QRD-M)-based noniterative QML receiver is employed in this paper. We first illustrate that there is a large discrepancy between the amplitude of the raw log-likelihood ratio (LLR) generated by the complexity-constrained QRD-M detector and its reliability, particularly for LLRs with high amplitudes. The discrepancy is shown to be the main underlying cause of degraded performance in coded MIMO systems. Then, we propose a reliability-aware transformation technique, which exploits the statistical relationship between the raw LLR's amplitude and its reliability to adjust the raw LLR's amplitude to a level commensurate with its reliability. By using the proposed statistical knowledge-based technique, the performance of the receiver (detector plus decoder) recorded at the output of the channel decoder is significantly enhanced for high-order modulations. The extension of the proposed method to other QML receivers such as the list sequential (LISS) detector and list sphere detector (LSD)-based receivers is straightforward. [ABSTRACT FROM AUTHOR]