RLS Precoding for Massive MIMO Systems with Nonlinear Front-End

Abstract

To keep massive MIMO systems cost-efficient, power amplifiers with rather small output dynamic ranges are employed. They may distort the transmit signal and degrade the performance. This paper proposes a distortion aware precoding scheme for realistic scenarios in which RF chains have nonlinear characteristics. The proposed scheme utilizes the method of regularized least-squares (RLS) to jointly compensate the channel impacts and the distortion imposed by the RF chains.To construct the designed transmit waveform with low computational complexity, an iterative algorithm based on approximate message passing is developed. This algorithm is shown to track the achievable average signal distortion of the proposed scheme tightly, even for practical system dimensions. The results demonstrate considerable enhancement compared to the state of the art.

A Tutorial on Interference Exploitation via Symbol-Level Precoding: Overview, State-of-the-Art and Future Directions

“…In this context, the discussed CEP schemes are an effective strategy, since they achieve the best possible PAPR (unit) at the symbol level. Meanwhile, alternative schemes based on SLP have also been proposed in the literature for non-linear channels in [135], [301]- [307], where the aim is not to attain a CE transmission, but rather to optimize the power dynamics (such as the dynamic range and PAPR) following an optimization framework in line with [125], [228] based on per-user QoS constraints. The main idea of these schemes is to control the instantaneous transmit power and to minimize its peaks, both in the temporal dimension and in the spatial one (i.e.…”

Robust Hierarchical-Optimization RLS Against Sparse Outliers

“…a probability space [17], and denotes vector/matrix transposition. The multiple-input-multiple-output model ( 1) is chosen here to offer a general model that is able to capture data-generation mechanisms in numerous modern application domains, e.g., [18,19] and [1, p. 647]. To save space, y n , x n , o n and v n represent both random variables (RVs) and their realizations.…”