Sparse Linear Precoders For Mitigating Nonlinearities In Massive MIMO - Mezghani, Amine; Plabst, Daniel; Swindlehurst, Lee A.; Fijalkow, Inbar; Nossek, Josef A. (IEEE Statistical Signal Processing Workshop, 2021)
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/Aconverters (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.