# Optimally-Tuned Nonparametric Linear Equalization for Massive MU-MIMO Systems

## TL;DR

This paper introduces NOPE, a new nonparametric linear equalizer for massive MU-MIMO systems that matches L-MMSE performance without needing transmit or noise power knowledge, enhancing practicality.

## Contribution

The paper proposes NOPE, a novel, optimally-tuned nonparametric equalizer that eliminates the need for parameter estimation in massive MU-MIMO systems.

## Key findings

- NOPE achieves L-MMSE performance in large-antenna limits.
- NOPE is computationally efficient and practical.
- NOPE performs well in finite-dimensional systems.

## Abstract

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