Improving MIMO detection performance in presence of phase noise using norm difference criterion
Abstract
— Practical MIMO communication systems suffer performance loss from oscillator phase noise. In particular, traditional maximum likelihood (ML) detection algorithm results in an error floor in symbol error probability, and thus becomes unable to harvest the spatial diversity to be obtained in MIMO systems without phase noise. In this paper, we propose a method to detect the correctness of the traditional ML solution in the presence of strong phase noise. A criteria based on the ML cost differences between the ML solution and the next best solutions is used to determine a set of possible candidate solutions. We also propose a novel algorithm for data detection using phase noise estimation techniques to obtain an modified ML cost for each of the candidate solutions. This approach results in symbol error rate performance improvement by reducing the error floor without incurring much additional complexity due to phase noise estimation. Theoretical arguments as well as simulation studies are presented to support the performance improvement achieved.
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