A Soft-Output MIMO Detector With Achievable Information Rate based Partial Marginalization

In this paper, we propose a soft-output detector for multiple-input multiple-output (MIMO) channels that utilize achievable information rate (AIR) based partial marginalization (PM). The proposed AIR based PM (AIR-PM) detector has superior performance compared to previously proposed PM designs and other soft-output detectors such as K-best, while at the same time yielding lower computational complexity, a detection latency that is independent of the number of transmit layers, and straightforward inclusion of soft-input information. Using a tree representation of the MIMO signal, the key property of the AIR-PM is that the connections among all child layers are broken. Therefore, least-square estimates used for marginalization are obtained independently and in parallel, which have better quality than the zero-forcing decision feedback estimates used in previous PM designs. Such a property of the AIR-PM detector is designed via a mismatched detection model that maximizes the AIR. Furthermore, we show that the chain rule holds for the AIR calculation, which facilitates an information theoretic characterization of the AIR-PM detector.