Learning Energy-Efficient Modular Arrays under Hardware Non-linearities

TL;DR

This paper presents a deep learning approach to optimize energy efficiency in large aperture array systems considering hardware non-linearities, achieving significant gains over traditional methods.

Contribution

It introduces a neural network that jointly predicts power allocation and array activation to maximize energy efficiency under non-linear hardware effects.

Findings

DNN-based arrays outperform conventional sparse arrays in energy efficiency.

Analytical spectral efficiency expression derived for non-linear MIMO channels.

Joint optimization improves system performance significantly.

Abstract

This paper investigates the joint optimization of power allocation and antenna activation in sparse extremely large aperture array systems operating under power amplifier non-linearities. We first derive an analytical expression for the achievable spectral efficiency (SE) of point-to-point MIMO channels affected by non-linear distortions using the Bussgang decomposition. To address the combinatorial and non-convex nature of the energy-efficiency (EE) maximization problem, we employ an unsupervised deep neural network (DNN) that learns the non-linear mapping between the channel state information and the optimal EE operating point. The DNN jointly predicts distortion-aware power allocation, total transmit power scaling, and modular sub-array activation based on singular-value and geometric channel features. Numerical results demonstrate that the proposed DNN-based arrays achieve significant EE gains over the conventional sparse arrays.