A Near-Field Super-Resolution Network for Accelerating Antenna Characterization

TL;DR

This paper introduces NFS-Net, a deep learning model that reconstructs high-resolution near-field antenna data from undersampled measurements, significantly reducing sampling requirements and accelerating antenna characterization.

Contribution

The paper presents a novel deep neural network that enables super-resolution of near-field data, reducing sampling needs and improving measurement efficiency in antenna characterization.

Findings

NFS-Net accurately reconstructs high-resolution data from low-resolution inputs.

The combined NFS-Net and NF2FF workflow achieves accurate far-field patterns with only 11% of traditional samples.

The method demonstrates good generalizability across different measurement setups.

Abstract

We present a deep neural network-enabled method to accelerate near-field (NF) antenna measurement. We develop a Near-field Super-resolution Network (NFS-Net) to reconstruct significantly undersampled near-field data as high-resolution data, which considerably reduces the number of sampling points required for NF measurement and thus improves measurement efficiency. The high-resolution near-field data reconstructed by the network is further processed by a near-field-to-far-field (NF2FF) transformation to obtain far-field antenna radiation patterns. Our experiments demonstrate that the NFS-Net exhibits both accuracy and generalizability in restoring high-resolution near-field data from low-resolution input. The NF measurement workflow that combines the NFS-Net and the NF2FF algorithm enables accurate radiation pattern characterization with only 11% of the Nyquist rate samples. Though the experiments in this study are conducted on a planar setup with a uniform grid, the proposed method can serve as a universal strategy to accelerate measurements under different setups and conditions.