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Title: Optimally-tuned nonparametric linear equalization for massive MU-MIMO systems
This 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.  more » « less
Award ID(s):
1652065
PAR ID:
10049094
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
IEEE International Symposium on Information Theory (ISIT)
Page Range / eLocation ID:
2118 to 2122
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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