Machine Learning Based Source Reconstruction for RF Desense

Huang, Qiaolei; Fan, Jun

doi:10.1109/temc.2018.2797132

Cited by 61 publications

(6 citation statements)

References 14 publications

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“…It requires understanding of dipole moment radiation to recognize the location and type of dipole moment from the radiation patterns; sometimes it is not even possible, especially when multiple dipole moments exist. For this case, the physical dipole can be reconstructed based on the radiation pattern recognition, and the recognition can be automated by artificial intelligence based on machine learning algorithm [9].…”

Section: Noise Source Modelmentioning

confidence: 99%

RF Desensitization in Wireless Devices

Hwang¹

2019

RF Systems, Circuits and Components

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The Internet of Things (IoT), where data are exchanged via wireless connection between devices, is rapidly becoming inextricable from our daily lives. A variety of IoT devices ranging from smart homes to autonomous vehicles and health care have grown explosively. While wireless communication makes the devices conveniently connected, it also makes them inherently vulnerable to electromagnetic interference (EMI). Any radio frequency (RF) antenna used as a radio receiver can easily pick up the unintended electromagnetic noise from integrated circuits (ICs) populated within the same device. The radio range is limited by interference, called RF desensitization, which in turn often limits the usefulness of IoT devices. While the amount of EMI can be estimated using numerical simulations tools like HFSS and CST, engineering issues such as where to place the IC or setting the radiation specification of the IC cannot be so easily addressed. In this chapter, an insightful and efficient RF desensitization model necessary to estimate EMI levels on RF antennas will be addressed. The approach will be focused on two representative areas: noise radiation source modeling and coupling estimation associated with an embedded RF antenna.

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Section: Noise Source Modelmentioning

confidence: 99%

RF Desensitization in Wireless Devices

Hwang¹

2019

RF Systems, Circuits and Components

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“…This method can extract the primary dipole sources one by one, thus having better robustness and higher reliability compared with the traditional least squares method. 16 The free space Green's function is generally used to relate the magnetic dipole moment to the scanned near magnetic field, and then the equivalent dipole model can be readily constructed by solving the corresponding matrix equation and inverse problem. Generally, the inverse problem can be solved in a variety of ways, including global optimization method, least squares method, and TRM.…”

Section: Introductionmentioning

confidence: 99%

A highly accurate and robust source reconstruction method of printed circuit boards based on complex‐valued neural network

Zhang,

Nie,

Wang

et al. 2023

Circuit Theory & Apps

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SummaryPrinted circuit boards play an increasingly important role in modern electronic systems. Equivalent dipole moments are widely used to reconstruct the radiated fields of printed circuit boards in view of the extremely high complexity. In this paper, an improved source reconstruction method based on complex‐valued neural network is proposed to investigate the radiation properties of printed circuit boards working in microwave frequency band. Firstly, the magnetic field data obtained from the near‐field scanning are utilized to construct a matrix equation based on the source reconstruction theory. Secondly, the kernel of the complex‐valued neural network is constructed through the algorithm of complex numbers, which eventually end up with the novel neural network with the help of the principle of regression optimization. Thirdly, the data obtained from the near‐field scanning are fed into the neural network for effective training to get the equivalent dipole model. Finally, several numerical examples and available measurement results are given to validate the method. Compared with the commonly used Tikhonov regularization method, the proposed method possesses higher accuracy and better robustness in noise suppression.

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“…Research on the magnitude-only near-field scanning-based SRM has become a hotspot recently due to the dispensation from phase measurement [11]- [13]. Besides, machine learning has been introduced into the conventional SRM to improve the accuracy and reliability of the extracted equivalent sources [14]. In the past few years, the time reversal has been applied to reconstruct multiple EM source locations and amplitudes [15].…”

Section: Introductionmentioning

confidence: 99%

Source Reconstruction of Electronic Circuits in Shielding Enclosures Based on Numerical Green’s Function and Application in Electromagnetic Interference Estimation

Wang

Xiao

Mao

et al. 2022

IEEE Trans. Microwave Theory Techn.

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In this work, to characterize the radiated emission from electronic circuits in shielding enclosure, an improved electric dipole-based source reconstruction method (SRM) is developed. Moreover, by resorting to this reconstructed equivalent source, the estimation of electromagnetic interference (EMI) between different circuit modules in the enclosure can be conveniently and accurately evaluated. Different from the freespace SRM, the equivalent dipoles of the proposed SRM are directly placed over the shielding box enclosed circuit board, and the numerical Green's function (NGF) is developed to bridge the connection between the equivalent dipoles and the planar scanned magnetic near-field. With the NGF strategy, the effects of surrounding environments (including the presence of substrate, ground plane, and shielding enclosure) are inclusively considered, which makes the proposed SRM valid for any complex EM environments in theory. Since the proposed SRM also has the capability of reproducing the radiated emission inside the shielding enclosure, it is critically helpful to evaluate the EMI between different circuit modules inside the shielding enclosure. To reach this aim, the Rayleigh-Carson Reciprocity theorem is referred, in which the original EM coupling problem is decomposed into the "forward problem" and "reverse problem." A Huygens box enclosing the victim circuit module is built and the EM fields on the box are obtained using the reconstructed dipoles in both problems. Then the EMI can be calculated accordingly. Therefore, the novelty and merits of the proposed approach are threefold: 1) the physically-based equivalent dipole model considers the interactions between the radiation sources and the surrounding objects, leading to the accurate prediction of radiated emissions both outside and inside the enclosure; 2) due to the utilization of the NGF, there is no approximation involved in the solution of the dipole's radiation, thus improving Manuscript

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Machine Learning Based Source Reconstruction for RF Desense

Cited by 61 publications

References 14 publications

RF Desensitization in Wireless Devices

RF Desensitization in Wireless Devices

A highly accurate and robust source reconstruction method of printed circuit boards based on complex‐valued neural network

Source Reconstruction of Electronic Circuits in Shielding Enclosures Based on Numerical Green’s Function and Application in Electromagnetic Interference Estimation

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