Predicting Power Density for Mm‐Wave Handset Antennas Based on Machine Learning

ABSTRACT Simulating the peak spatial‐average incident power density (PD) for 5G millimeter‐wave (mm‐wave) handset antenna arrays is important but time‐consuming, as the entire handset needs to be taken into account. In this letter, a time‐efficient method based on machine learning (ML) is proposed to predict the maximum PD of mm‐wave antennas in real handsets. By training various typical antenna arrays, a mapping relationship is established between the near‐field distributions of the antenna array in real handsets and those of the stand‐alone array. With this mapping, the maximum PD of any mm‐wave array in a real handset can be quickly predicted by simulating the PD of the corresponding stand‐alone array. The method has been verified on several 4 × 1 arrays commonly used in 5G mobile handsets, showing accurate prediction of the maximum PD. The proposed method can be applied to mm‐wave arrays of any type and significantly reduces the evaluation time.