Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO

TL;DR

This paper introduces a sparse linear precoding technique for massive MIMO systems that effectively mitigates RF nonlinearities, reduces PAPR, and lowers processing complexity, enhancing system performance.

Contribution

The paper proposes a novel sparse regularization-based linear precoder tailored for large-scale MIMO to address RF nonlinearities and hardware constraints.

Findings

Significant reduction in PAPR compared to traditional precoders

Improved system performance demonstrated through simulations

Lower computational complexity of the proposed method

Abstract

Dealing with nonlinear effects of the radio-frequency(RF) chain is a key issue in the realization of very large-scale multi-antenna (MIMO) systems. Achieving the remarkable gains possible with massive MIMO requires that the signal processing algorithms systematically take into account these effects. Here, we present a computationally efficient linear precoding method satisfying the requirements for low peak-to-average power ratio (PAPR) and low-resolution D/A-converters (DACs). The method is based on a sparse regularization of the precoding matrix and offers advantages in terms of precoded signal PAPR as well as processing complexity. Through simulation, we find that the method substantially improves conventional linear precoders.