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Mismatched Data Detection in Massive MU-MIMO

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Document pages: 11 pages

Abstract: We investigate mismatched data detection for massive multi-user (MU)multiple-input multiple-output (MIMO) wireless systems in which the priordistribution of the transmit signal used in the data detector differs from thetrue prior. In order to minimize the performance loss caused by the priormismatch, we include a tuning stage into the recently proposed large-MIMOapproximate message passing (LAMA) algorithm, which enables the development ofdata detectors with optimal as well as sub-optimal parameter tuning. We showthat carefully-selected priors enable the design of simpler and computationallymore efficient data detection algorithms compared to LAMA that uses the optimalprior, while achieving near-optimal error-rate performance. In particular, wedemonstrate that a hardware-friendly approximation of the exact prior enablesthe design of low-complexity data detectors that achieve nearindividually-optimal performance. Furthermore, for Gaussian priors and uniformpriors within a hypercube covering the quadrature amplitude modulation (QAM)constellation, our performance analysis recovers classical and recent resultson linear and non-linear massive MU-MIMO data detection, respectively.

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