Dan Shi

This article introduces a novel deep learning approach, utilizing a seq2seq model, to predict far-field radiation emissions (REs) in high-density, multilayer, and complex printed circuit boards (PCBs). To address the challenges of weak long-term dependencies and information ambiguity arising from excessively long network sequences in RE prediction for complex PCBs, our method begins by tokenizing and modeling each network combination. These combinations are then fed into the seq2seq model for training, supplemented with a multihead attention mechanism to enhance information connectivity. To validate the model's accuracy and efficiency, we compared our prediction results with those obtained from traditional full-wave electromagnetic simulations. Our approach significantly reduces the RE prediction time for the entire board from several hours to nearly one second. Moreover, the model exhibits high performance in RE prediction, with a mean absolute error of approximately 0.001 V/m. To verify the model's reliability, we applied the trained model to predict RE for brand-new entire boards and key networks, demonstrating its ability to accurately predict RE in previously unseen PCBs. Therefore, this method can be implemented for intelligent, full-automatic RE prediction in practical applications.

Fixed-wing unmanned aerial vehicles (UAVs) are widely considered as a vital candidate of aerial base station in beyond Fifth Generation (B5G) systems. Accurate knowledge of air-to-ground (A2G) wireless propagation is important for A2G communication system development and testing where, however, there is still a lack of A2G wideband channel models for such a purpose. In this paper, we present a wideband fixed-wing UAV-based A2G channel measurement campaign at 2.7 GHz, and consider typical flight phases, based on which a wide-sense stationary uncorrelated scattering (WSSUS)-based tapped delay line (TDL) wideband channel model is proposed. Parameters of individual channel taps are analyzed in terms of gain, amplitude distribution, Rice factor and delay-Doppler spectrum. It is shown that UAV flight phases significantly influence the channel tap parameters. Particularly, the “Bell”-type spectrum is found to be the most suitable model for the delay-Doppler spectrum under various flight scenarios for A2G propagation. The proposed channel model can provide valuable assistance and guidance for UAV communication system evaluation and network planning.

This article introduces a novel crosstalk prediction method, defines a standard data format for printed circuit board (PCB) modeling, and establishes an efficient computation model utilizing machine learning. The crosstalk prediction is undertaken by using a bidirectional long short-term memory (Bi-LSTM) model and is enhanced with an attention mechanism. To validate the model's precision and efficiency, we compare its prediction results with the results of traditional full-wave electromagnetic simulations. The model demonstrates outstanding crosstalk prediction capabilities, achieving an accuracy exceeding 95.72%. Notably, the proposed method diminishes the full board prediction time from hours to mere seconds. To verify the model's reliability, the trained model was used to predict the crosstalk of three unseen PCBs and accurately identified the potential crosstalk in the unseen PCB. Thus, the proposed method can practically be applied to a fully automated and intelligent electromagnetic compatibility design of electronic products.

A novel method based on machine learning is proposed to estimate the electromagnetic radiation level at the ground plane near fifth‐generation (5G) base stations. The machine learning model was trained using data from various 5G base stations, enabling it to estimate the electric field intensity at any arbitrary radiation point when the base station provides service to different numbers of 5G terminals which are in different service modes. The inputs required for the model include the transmit power of the antenna, the antenna gain, the distance between the 5G base station and 5G terminals, terminal service modes, the number of 5G terminals and the environmental complexity around the 5G base station. Experimental results demonstrate the feasibility and effectiveness of the estimation method, with the mean absolute percentage error of the machine learning model being approximately 5.98%. This level of accuracy showcases the reliability of the approach. Moreover, the proposed method offers low costs when compared with on‐site measurements. The estimated results can be utilised to reduce test costs and provide valuable guidance for optimal site selection, thereby facilitating radio wave coverage or electromagnetic radiation regulation of 5G base stations.

As one of the most useful methods in electromagnetic interference (EMI) diagnosis, near-field (NF) scanning is widely used in electromagnetic compatibility (EMC) evaluation of complex devices under test (DUTs). In this paper, a two-stage plane adaptive sampling algorithm is proposed to reduce the acquisition time in the process of NF scanning and to make reconstruction of the radiation source more efficient. The sampling method is based on the region self-growth algorithm and the Voronoi subdivision principle, significantly reducing the number of NF samples in the stage of solving the radiation source model through uniform and non-uniform two-stage sampling. Two experiments were conducted to verify the correctness and effectiveness by comparing with the traditional uniform sampling method.

With the rise of autonomous driving technology in recent years, millimeter wave radar has been more and more widely used in automobiles. In complex actual road conditions, the mutual interference between a large number of vehicle-mounted FMCW radars working in the same frequency band, as well as the existence of some human intentional or unintentional interference in special scenarios may make the target detection error. In this paper, based on the working mechanism of FMCW radar, the influence process of interference on the speed detection results is deduced, and a generalized interference analysis platform is proposed to do quantitative calculations on the influence of interference signals of different frequencies and intensities on speed detection. Through the research of this paper can be more in-depth understanding of the role of interference on the target judgment mechanism, for radar anti-jamming algorithm and target judgment algorithm research to provide reference basis.

An intelligent antenna synthesis method is proposed to automatically select suitable antenna type and provide optimal geometric parameters according to the requirement of antenna performance. The performance characteristics of different antennas are analyzed, and a smart antenna selection model is established to recommend the appropriate antenna by using support vector machine (SVM). Then, the stacking ensemble model is built by combining five primary learners to give full play to the advantages of each type of learners and, finally, the geometric parameters of the antenna are provided. Microstrip patch antenna, spiral antenna, and horn antenna are used to demonstrate the accuracy and efficiency of the model. The system shows a great ability of antenna classification with accuracy over 99% and parameter prediction with a mean absolute percentage error (MAPE) of less than 6%. Moreover, the results are compared with the traditional data-driven model, and the key parameters of the model are comprehensively discussed. The proposed method could be widely used in intelligent antenna design in practice.

A compact and high-performance shielding enclosure designed by metamaterial structure based on frequency selective surface (FSS) is proposed. The enclosure has large holes for convenience of airflow and cable access. However, it can achieve great shielding performance by maintaining more than 40 dB attenuation. The shield is composed of n ×× n unit cells, and each unit cell is designed by knitting the 2.5-dimensional loop-type elements interconnected through vias. This design shows promising capability of size reduction, bandwidth expansion, and shielding effectiveness enhancement. Moreover, the enlarged holes on the FSS are helpful for the ventilation and heat dissipation. The size of the proposed 2.5-D FSS is only 0.097λ0λ0××0.097λ0λ0, where λ0λ0 corresponds to free space wavelength of resonance frequency. The proposed structure provides 3.38 GHz (3.21–6.59 GHz) wide shielding bandwidth. Furthermore, it has stable response to the wide-angle incident wave ranging from 0∘∘ to 85∘∘ with more than 40 dB attenuation at 4.83 GHz for both x-polarization and y-polarization. The proposed FSS is practically useful for the shielding of fifth generation (5G) wireless systems, WiMAX, and WLAN.

Due to the diversity of landforms, an appropriate threshold should be selected for the simplification of different landforms. The distribution of simplified index was obtained by preprocessing the terrain data, and the distribution was used as a reference for threshold selection. In order to study the influence of terrain simplification on the prediction of complex electromagnetic environment, a visibility algorithm used in the regular model of terrain and a probability-based power propagation model are proposed. The visibility algorithm used in the regular model of terrain is based on the relationship between planes and triangular planes in three-dimensional space. The visible triangular surfaces and the corresponding visible area of the triangular surfaces in the terrain can be calculated with the visibility algorithm. The probability-based power propagation model uses the results of the visibility algorithm to calculate the propagation probability and calculates the change of receiver power in combination with the radio wave propagation characteristics. The spatial visibility algorithm and the probability-based power propagation model can be applied to the complex electromagnetic environment to analyze the influence of terrain simplification on prediction accuracy. The minimum error between the simulation result of the probability-based power propagation model and the Wireless InSite simulation result is 0.5018dB, and the average error is 1.5039dB.