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

We propose a statistical information based approach to enhance the performance of the non-iterative quasi-maximumlikelihood (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 non-iterative QML receiver is employed in this correspondence. 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, especially 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 reliabilityaware transformation technique which exploits the statistical relationship between the raw LLR amplitude and its reliability to adjust the raw LLR 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 quasi-ML receivers, such as the list sequential (LISS) detector and list sphere detector (LSD) based receivers, is straightforward.

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