Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2504.17065v1
• Date: Wed, 23 Apr 2025 19:23:37 GMT
• Title: Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks
• Authors: Sahar Bagherkhani, Jackson Christopher Earls, Franco De Flaviis, Pierre Baldi,
• Abstract summary: This paper presents a deep learning-based approach for Far-Field to Near-Field (FF-NF) transformation using Convolutional Neural Networks (CNNs)<n>CNNs are trained on paired far-field and near-field data and evaluated using mean squared error (MSE)<n>Visual comparisons between the predicted and true near-field distributions demonstrate the model's effectiveness in capturing complex electromagnetic field behavior.
• Score: 4.974500659156055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electromagnetic field reconstruction is crucial in many applications, including antenna diagnostics, electromagnetic interference analysis, and system modeling. This paper presents a deep learning-based approach for Far-Field to Near-Field (FF-NF) transformation using Convolutional Neural Networks (CNNs). The goal is to reconstruct near-field distributions from the far-field data of an antenna without relying on explicit analytical transformations. The CNNs are trained on paired far-field and near-field data and evaluated using mean squared error (MSE). The best model achieves a training error of 0.0199 and a test error of 0.3898. Moreover, visual comparisons between the predicted and true near-field distributions demonstrate the model's effectiveness in capturing complex electromagnetic field behavior, highlighting the potential of deep learning in electromagnetic field reconstruction.

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