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

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.

Flexible Quasi-Yagi-Uda antenna for 5G communication

This paper presents the design and experimental of a single and array Quasi Yagi-Uda antenna at 28 GHz. The proposed antenna is implemented on MFLEX flexible material with a thickness of 0.120mm for 5G applications. A wideband antenna operates within 24 to 29.5 GHz and exhibits almost the same end-fire radiation pattern over bandwidth with an average gain of 6.2dBi and 10.15dBi for single and array antennas. The flexible antenna was tested under bending conditions and results showed excellent performance at the 28GHz region