Conference PaperOptimally-tuned nonparametric linear equalization for massive MU-MIMO systemsGhods, Ramina; Jeon, Charles; Mirza, Gulnar; Maleki, Arian; Studer, ChristophThis paper deals with linear equalization in massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems. We first provide simple conditions on the antenna configuration for which the well-known linear minimum mean-square error (L-MMSE) equalizer provides near-optimal spectral efficiency, and we analyze its performance in the presence of parameter mismatches in the signal and/or noise powers. We then propose a novel, optimally-tuned NOnParametric Equalizer (NOPE) for massive MU-MIMO systems, which avoids knowledge of the transmit signal and noise powers altogether. We show that NOPE achieves the same performance as that of the L-MMSE equalizer in the large-antenna limit, and we demonstrate its efficacy in realistic, finite-dimensional systems. From a practical perspective, NOPE is computationally efficient and avoids dedicated training that is typically required for parameter estimation.2017-06-2510049094IEEE International Symposium on Information Theory (ISIT)2118 to 2122https://doi.org/10.1109/ISIT.2017.80069031652065National Science Foundation