Pub Date : 2025-08-29DOI: 10.1109/JMW.2025.3598684
Amjad Iqbal;Abdul Basir;Muath Al-Hasan;Ismail Ben Mabrouk;Tayeb A. Denidni
In this paper, a dual-antenna multiple-input multiple-output (MIMO) system is designed for deep-tissue indigestible capsules. The system operates at 915 MHz and 2450 MHz frequency bands, covering the desired ISM band. The 10-dB bandwidths are 210 MHz (840–1050 MHz) and 220 MHz (2350–2570 MHz). The antenna’s overall volume is 12.34 mm3, with a radius of 5.5 mm and a height of 0.13 mm. Compactness is achieved through the use of a high permittivity substrate, shorting pins, and multiple slots in the patch. A common ground plane with a rectangular slot in the middle is incorporated to reduce electromagnetic (EM) coupling between the antenna elements. Additionally, two inductors are placed between the antenna patches to further minimize the EM coupling. Inductor-I (8.5 nH) combined with a slot capacitance generates a transmission zero (TZ) at the lower frequency band, while Inductor-II (1 nH) combined with a slot capacitance generates a TZ at the higher frequency band. This design results in very low coupling values of $-$34.6 dB at 915 MHz and $-$38.92 dB at 2450 MHz. The antenna achieves peak realized gains of $-$31.5 dBi at 915 MHz and $-$22.3 dBi at 2450 MHz. With 1 W incident power, SAR values of 43.1 W/kg at 915 MHz and 46.3 W/kg at 2450 MHz are observed. The envelope correlation coefficient is less than 0.1 in both bands, making this antenna suitable for high-speed communication in ingestible implants.
{"title":"Highly Isolated Multiple-Input Multiple-Output Antenna System for Ingestible Implants","authors":"Amjad Iqbal;Abdul Basir;Muath Al-Hasan;Ismail Ben Mabrouk;Tayeb A. Denidni","doi":"10.1109/JMW.2025.3598684","DOIUrl":"https://doi.org/10.1109/JMW.2025.3598684","url":null,"abstract":"In this paper, a dual-antenna multiple-input multiple-output (MIMO) system is designed for deep-tissue indigestible capsules. The system operates at 915 MHz and 2450 MHz frequency bands, covering the desired ISM band. The 10-dB bandwidths are 210 MHz (840–1050 MHz) and 220 MHz (2350–2570 MHz). The antenna’s overall volume is 12.34 mm<sup>3</sup>, with a radius of 5.5 mm and a height of 0.13 mm. Compactness is achieved through the use of a high permittivity substrate, shorting pins, and multiple slots in the patch. A common ground plane with a rectangular slot in the middle is incorporated to reduce electromagnetic (EM) coupling between the antenna elements. Additionally, two inductors are placed between the antenna patches to further minimize the EM coupling. Inductor-I (8.5 nH) combined with a slot capacitance generates a transmission zero (TZ) at the lower frequency band, while Inductor-II (1 nH) combined with a slot capacitance generates a TZ at the higher frequency band. This design results in very low coupling values of <inline-formula><tex-math>$-$</tex-math></inline-formula>34.6 dB at 915 MHz and <inline-formula><tex-math>$-$</tex-math></inline-formula>38.92 dB at 2450 MHz. The antenna achieves peak realized gains of <inline-formula><tex-math>$-$</tex-math></inline-formula>31.5 dBi at 915 MHz and <inline-formula><tex-math>$-$</tex-math></inline-formula>22.3 dBi at 2450 MHz. With 1 W incident power, SAR values of 43.1 W/kg at 915 MHz and 46.3 W/kg at 2450 MHz are observed. The envelope correlation coefficient is less than 0.1 in both bands, making this antenna suitable for high-speed communication in ingestible implants.","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 5","pages":"1041-1052"},"PeriodicalIF":4.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-27DOI: 10.1109/JMW.2025.3596473
Chun Yin Lai;Steve W. Y. Mung;Lok Ki Ho;Anding Zhu
In this article, a simple deep neural network (DNN) is proposed to predict the linearity of power amplifier modules (PAMs) in load-pull measurement for mobile front-end impedance matching, not for power amplifier design by transistors. PAM is a crucial and fully matched packaged product in the transmitter for amplification in mobile products, which contains digital control circuits, passive components, RF switches, and multiband power amplifiers (PAs). For the 3GPP standard with low current consumption to be met, load-pull measurement of the PAM is essential for the mobile front-end impedance matching application to optimize the final product. However, traditional measurement using all impedance points for plotting load-pull contours is time-consuming. Compared with the traditional measurement method, the proposed method can minimize the measurement time by more than half. The impedance points used for the load-pull measurement are randomly split into two datasets with different ratios for verification. A set of impedance points is used for DNN model training. Another set of impedance points is used for linearity prediction. Experiments have been conducted, and the results highlight that the proposed DNN approach has high accuracy in linearity prediction and significantly minimizes the load-pull data measurement time, almost by half compared with the traditional measurement method. This study demonstrates the effectiveness of DNN with simple MLP structure in load-pull contour exploration in mobile front-end impedance matching applications.
{"title":"Deep Neural Network-Based Load-Pull Measurement for Linearity Prediction in Mobile Front-End Impedance Matching Application","authors":"Chun Yin Lai;Steve W. Y. Mung;Lok Ki Ho;Anding Zhu","doi":"10.1109/JMW.2025.3596473","DOIUrl":"https://doi.org/10.1109/JMW.2025.3596473","url":null,"abstract":"In this article, a simple deep neural network (DNN) is proposed to predict the linearity of power amplifier modules (PAMs) in load-pull measurement for mobile front-end impedance matching, not for power amplifier design by transistors. PAM is a crucial and fully matched packaged product in the transmitter for amplification in mobile products, which contains digital control circuits, passive components, RF switches, and multiband power amplifiers (PAs). For the 3GPP standard with low current consumption to be met, load-pull measurement of the PAM is essential for the mobile front-end impedance matching application to optimize the final product. However, traditional measurement using all impedance points for plotting load-pull contours is time-consuming. Compared with the traditional measurement method, the proposed method can minimize the measurement time by more than half. The impedance points used for the load-pull measurement are randomly split into two datasets with different ratios for verification. A set of impedance points is used for DNN model training. Another set of impedance points is used for linearity prediction. Experiments have been conducted, and the results highlight that the proposed DNN approach has high accuracy in linearity prediction and significantly minimizes the load-pull data measurement time, almost by half compared with the traditional measurement method. This study demonstrates the effectiveness of DNN with simple MLP structure in load-pull contour exploration in mobile front-end impedance matching applications.","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 5","pages":"1137-1149"},"PeriodicalIF":4.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-20DOI: 10.1109/JMW.2025.3595622
Mengting Jiang;Daying Quan;Fang Zhou;Kaiyin Yu;Yi Chen;Ning Jin
Deep learning has been extensively used in radar signal modulation recognition, leading to significant improvements in accuracy. For supervised methods, the recognition performance mainly depends on the quality of large-scale labeled data. However, data annotation is usually expensive and time-consuming. The acquisition of high-quality labeled data poses a significant challenge. To address this issue, this paper proposes a radar signal modulation recognition method based on multimodal contrastive learning (RS-MCL). First, we obtain the feature of radar signal by performing pre-training based on contrastive learning with unlabeled multimodal radar signals. Then, the pre-trained encoder is fine-tuned along with a randomly initialized classifier to finish the recognition task, where only a small number of labeled samples are fed. Given the characteristics of multimodal inputs, two distinct attention mechanisms are incorporated in the encoder to effectively extract features from both the time-domain signal and time-frequency image. Experimental results demonstrate the superiority and stability of the proposed method across most of signal-to-noise ratio (SNR) conditions, even when utilizing only 1% of the labeled samples.
{"title":"Modulation Recognition of Radar Signals Based on Multimodal Contrastive Learning","authors":"Mengting Jiang;Daying Quan;Fang Zhou;Kaiyin Yu;Yi Chen;Ning Jin","doi":"10.1109/JMW.2025.3595622","DOIUrl":"https://doi.org/10.1109/JMW.2025.3595622","url":null,"abstract":"Deep learning has been extensively used in radar signal modulation recognition, leading to significant improvements in accuracy. For supervised methods, the recognition performance mainly depends on the quality of large-scale labeled data. However, data annotation is usually expensive and time-consuming. The acquisition of high-quality labeled data poses a significant challenge. To address this issue, this paper proposes a radar signal modulation recognition method based on multimodal contrastive learning (RS-MCL). First, we obtain the feature of radar signal by performing pre-training based on contrastive learning with unlabeled multimodal radar signals. Then, the pre-trained encoder is fine-tuned along with a randomly initialized classifier to finish the recognition task, where only a small number of labeled samples are fed. Given the characteristics of multimodal inputs, two distinct attention mechanisms are incorporated in the encoder to effectively extract features from both the time-domain signal and time-frequency image. Experimental results demonstrate the superiority and stability of the proposed method across most of signal-to-noise ratio (SNR) conditions, even when utilizing only 1% of the labeled samples.","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 5","pages":"1082-1093"},"PeriodicalIF":4.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11130720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel approach to low noise amplifier (LNA)-antenna co-design in the Q-band frequency range, leveraging the antenna as an integral part of the LNA matching network to achieve broadband noise figure improvement.Unlike conventional designs, the proposed LNA does not include a 50$,Omega$ input matching network, allowing direct access to the complex frequency-dependent impedance (${Gamma _{opt}}$) associated with the LNA’s minimal noise figure (${NF_{min}}$). The antenna input impedance is optimized to match the LNA for minimal noise contribution, effectively enhancing system performance. Noise figure measurements of the active antenna prototype confirm the achievement of ${NF_{min}}$, ranging from 1.9 to 1.4 dB within the 35 to 40 GHz frequency band. Additionally, the receiver system demonstrates a gain of 14.5 dB and a noise figure below 3.6 dB across the operating frequency range. These results validate the effectiveness of the proposed co-design approach in reducing noise while maintaining high gain, making it a promising solution for next-generation millimeter-wave communication and sensing applications.
{"title":"Q-Band LNA-Antenna Co-Design: Exploiting Antenna Matching for System Noise Figure Optimization","authors":"Kirill Alekseev;Martin Johansson;Klas Eriksson;Bart Smolders;Roger Lozar;Remco Heijs;Ulf Johannsen","doi":"10.1109/JMW.2025.3588491","DOIUrl":"https://doi.org/10.1109/JMW.2025.3588491","url":null,"abstract":"This paper presents a novel approach to low noise amplifier (LNA)-antenna co-design in the Q-band frequency range, leveraging the antenna as an integral part of the LNA matching network to achieve broadband noise figure improvement.Unlike conventional designs, the proposed LNA does not include a 50<inline-formula><tex-math>$,Omega$</tex-math></inline-formula> input matching network, allowing direct access to the complex frequency-dependent impedance (<inline-formula><tex-math>${Gamma _{opt}}$</tex-math></inline-formula>) associated with the LNA’s minimal noise figure (<inline-formula><tex-math>${NF_{min}}$</tex-math></inline-formula>). The antenna input impedance is optimized to match the LNA for minimal noise contribution, effectively enhancing system performance. Noise figure measurements of the active antenna prototype confirm the achievement of <inline-formula><tex-math>${NF_{min}}$</tex-math></inline-formula>, ranging from 1.9 to 1.4 dB within the 35 to 40 GHz frequency band. Additionally, the receiver system demonstrates a gain of 14.5 dB and a noise figure below 3.6 dB across the operating frequency range. These results validate the effectiveness of the proposed co-design approach in reducing noise while maintaining high gain, making it a promising solution for next-generation millimeter-wave communication and sensing applications.","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 5","pages":"1107-1119"},"PeriodicalIF":4.9,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11119346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-23DOI: 10.1109/JMW.2025.3583891
Tahir Bashir;Wei Li;Tian Xia
This study presents a compact dual-band cubic multi-input-multi-output (MIMO) antenna specifically designed for gastrointestinal (GI) tract capsule endoscopy and cardiac leadless pacemaker systems. The proposed cubic MIMO antenna operates across two frequency bands: 1.395 to 1.4 GHz and 2.4 to 2.4835 GHz. Comprising four individual antennas, it has overall dimensions of 5.12 × 5.12 × 4.6 ${text{mm}}^{text{3}}$, which makes it a compact cubic design, achieved by employing symmetrically embedded radiating patch slots. The strategic relocation of port, pin, and ground slot not only resulted in reduced coupling due to opposite current flow but also contributed to achieving excellent frequency tuning for all antenna elements in cubic configuration. Encapsulated within wireless implants with batteries, sensors, and device circuitry, the proposed MIMO antenna was simulated in both homogeneous and heterogeneous body phantoms, including the small intestine, large intestine, stomach, and heart. Experimental validation also conducted using minced pork yielded results that agree with simulations, demonstrating the MIMO antenna effective performance, including measured reflection coefficient (−22 dB, −19 dB), gain ($-$28.17 dBi, $-$18.15 dBi), −10 dB bandwidth (390 MHz, 670 MHz), minimal coupling (−23 dB, −24 dB), and fractional bandwidth (27%, 26%) at 1.3975 and 2.45 GHz, respectively. Each cubic element radiates in four opposite directions, enabling radiation diversity in all four directions, crucial for various body postures during movement. The specific absorption rate (SAR) is also calculated and confirmed to remain within very safe limits for human implantation. Furthermore, a communication link analysis established the reliability of the antenna in maintaining stable communication with an external device over an 10 m and 15 m radius at the respective resonant frequencies, achieving a high data transmission rate of 100 Mbps. Further evaluation, including envelope correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), and total active reflection coefficient (TARC), confirms the usefulness of the proposed MIMO. Consequently, this MIMO antenna emerges as a highly promising candidate with radiation diversity, high compactness, and self-isolation ability for several wireless biomedical implants.
{"title":"Radiation Diversity Enabled Self-Isolated Compact Dual-Band Cubic MIMO Antenna for Wireless Biomedical Implants in Variable and Dynamic Environment","authors":"Tahir Bashir;Wei Li;Tian Xia","doi":"10.1109/JMW.2025.3583891","DOIUrl":"https://doi.org/10.1109/JMW.2025.3583891","url":null,"abstract":"This study presents a compact dual-band cubic multi-input-multi-output (MIMO) antenna specifically designed for gastrointestinal (GI) tract capsule endoscopy and cardiac leadless pacemaker systems. The proposed cubic MIMO antenna operates across two frequency bands: 1.395 to 1.4 GHz and 2.4 to 2.4835 GHz. Comprising four individual antennas, it has overall dimensions of 5.12 × 5.12 × 4.6 <inline-formula><tex-math>${text{mm}}^{text{3}}$</tex-math></inline-formula>, which makes it a compact cubic design, achieved by employing symmetrically embedded radiating patch slots. The strategic relocation of port, pin, and ground slot not only resulted in reduced coupling due to opposite current flow but also contributed to achieving excellent frequency tuning for all antenna elements in cubic configuration. Encapsulated within wireless implants with batteries, sensors, and device circuitry, the proposed MIMO antenna was simulated in both homogeneous and heterogeneous body phantoms, including the small intestine, large intestine, stomach, and heart. Experimental validation also conducted using minced pork yielded results that agree with simulations, demonstrating the MIMO antenna effective performance, including measured reflection coefficient (−22 dB, −19 dB), gain (<inline-formula><tex-math>$-$</tex-math></inline-formula>28.17 dBi, <inline-formula><tex-math>$-$</tex-math></inline-formula>18.15 dBi), −10 dB bandwidth (390 MHz, 670 MHz), minimal coupling (−23 dB, −24 dB), and fractional bandwidth (27%, 26%) at 1.3975 and 2.45 GHz, respectively. Each cubic element radiates in four opposite directions, enabling radiation diversity in all four directions, crucial for various body postures during movement. The specific absorption rate (SAR) is also calculated and confirmed to remain within very safe limits for human implantation. Furthermore, a communication link analysis established the reliability of the antenna in maintaining stable communication with an external device over an 10 m and 15 m radius at the respective resonant frequencies, achieving a high data transmission rate of 100 Mbps. Further evaluation, including envelope correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), and total active reflection coefficient (TARC), confirms the usefulness of the proposed MIMO. Consequently, this MIMO antenna emerges as a highly promising candidate with radiation diversity, high compactness, and self-isolation ability for several wireless biomedical implants.","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 5","pages":"1053-1070"},"PeriodicalIF":4.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11091378","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-15DOI: 10.1109/JMW.2025.3581235
Anitha Gopi;Sruthi Pallathuvalappil;Elizabeth George;Alex James
This paper proposes a non-invasive way to detect the antenna type from its radiation patterns to cross-validate its proper functioning. Here, the radiation pattern of three types of antennas namely: a) Dipole Antenna, b) Monopole Antenna, and c) Patch Antenna are used for the study. The feature formation from radiation patterns is performed using pixel sampling. Hardware implementation of a $128times 128$ pixel array layout is performed using the SkyWater 130 PDK. The cross-validation of the antenna radiation pattern is performed using a 3D Memristive Convolutional Neural Network (3D-CNN). The simulations of the 3D-CNN are done based on Skywater 130 PDK, and the results are analysed. Here, due to the flexibility of concurrent reading and writing, the area, power and latency for the classification is getting reduced. The accuracy and robustness of AI/ML models are used for predicting the antenna type and are tested under various additive noise, such as a) Gaussian, b) White, c) Pink, d) Speckle and e) Salt and Pepper. The AI/ML models like a) Convolutional Neural Network (CNN) b) YOLOv8, c) VG-19 Net, d) Decision Tree, e) Naive Bayes, f) Random Forest and g) K-Nearest Neighbours (KNN) are used for the performance evaluation.
本文提出了一种从天线的辐射方向图中检测天线类型的非侵入性方法，以交叉验证天线的正常工作。本文采用三种天线的辐射方向图进行研究：a)偶极天线，b)单极天线，c)贴片天线。利用像素采样对辐射模式进行特征形成。使用SkyWater 130 PDK执行$128 × 128$像素阵列布局的硬件实现。天线辐射方向图的交叉验证使用3D记忆卷积神经网络（3D- cnn）进行。基于Skywater 130 PDK对3D-CNN进行了仿真，并对仿真结果进行了分析。在这里，由于并发读写的灵活性，分类的面积、功耗和延迟都得到了降低。AI/ML模型的准确性和鲁棒性用于预测天线类型，并在各种加性噪声下进行了测试，例如a)高斯噪声，b)白色噪声，c)粉红色噪声，d)斑点噪声和e)盐和胡椒噪声。使用卷积神经网络（CNN） b) YOLOv8, c) VG-19 Net, d)决策树，e)朴素贝叶斯，f)随机森林和g) k近邻（KNN）等AI/ML模型进行性能评估。
{"title":"Predicting Antenna Radiation Patterns and Types From Voxlated Measurements Using Neuro-Memristive 3D Crossbars","authors":"Anitha Gopi;Sruthi Pallathuvalappil;Elizabeth George;Alex James","doi":"10.1109/JMW.2025.3581235","DOIUrl":"https://doi.org/10.1109/JMW.2025.3581235","url":null,"abstract":"This paper proposes a non-invasive way to detect the antenna type from its radiation patterns to cross-validate its proper functioning. Here, the radiation pattern of three types of antennas namely: a) Dipole Antenna, b) Monopole Antenna, and c) Patch Antenna are used for the study. The feature formation from radiation patterns is performed using pixel sampling. Hardware implementation of a <inline-formula><tex-math>$128times 128$</tex-math></inline-formula> pixel array layout is performed using the SkyWater 130 PDK. The cross-validation of the antenna radiation pattern is performed using a 3D Memristive Convolutional Neural Network (3D-CNN). The simulations of the 3D-CNN are done based on Skywater 130 PDK, and the results are analysed. Here, due to the flexibility of concurrent reading and writing, the area, power and latency for the classification is getting reduced. The accuracy and robustness of AI/ML models are used for predicting the antenna type and are tested under various additive noise, such as a) Gaussian, b) White, c) Pink, d) Speckle and e) Salt and Pepper. The AI/ML models like a) Convolutional Neural Network (CNN) b) YOLOv8, c) VG-19 Net, d) Decision Tree, e) Naive Bayes, f) Random Forest and g) K-Nearest Neighbours (KNN) are used for the performance evaluation.","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 5","pages":"1120-1136"},"PeriodicalIF":4.9,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11080314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The widespread adoption of advanced driver assistance systems (ADAS) has increased the use of millimeter-wave (mmWave) radars in vehicles, raising concerns about potential electromagnetic field (EMF) exposure for pedestrians. International guidelines for human exposure have introduced absorbed power density (APD) and incident power density (IPD) as physical quantities for evaluating local exposure above 6 GHz. However, pedestrian exposure to automotive radars has been insufficiently investigated, particularly in vehicle–pedestrian interactions with radar operating while stationary. This study employed computational simulations and experimental measurements to evaluate the exposure from a 12 × 1 patch antenna array operating at 79 GHz. Exposure scenarios were analyzed using simplified geometric models and anatomically realistic human models at varying distances and equivalent isotropically radiated power (EIRP) levels. The results demonstrate a good agreement between the simulated and measured electric field distributions in both the near- and far-field regions. For continuous exposure, APD values obtained from anatomical models were consistently lower than those obtained from simplified geometries. At EIRPs of 26.7 dBm and 35.4 dBm, both APD and IPD remain within permissible limits across all distances. In contrast, the exposure at higher power levels (e.g., 55 dBm EIRP) exceeded the APD threshold. Nevertheless, evaluation using absorbed energy density, a metric for brief exposures, indicated compliance even when the human model was positioned directly adjacent to the vehicle surface. These findings provide critical insights into ensuring the conformity and design of next-generation automotive radar development.
{"title":"Conformity Assessment of Human Exposed to Radiation From Millimeter-Wave Vehicles Radars","authors":"Ryota Morimoto;Sachiko Kodera;Yuma Kobayashi;Keishi Miwa;Akimasa Hirata","doi":"10.1109/JMW.2025.3580722","DOIUrl":"https://doi.org/10.1109/JMW.2025.3580722","url":null,"abstract":"The widespread adoption of advanced driver assistance systems (ADAS) has increased the use of millimeter-wave (mmWave) radars in vehicles, raising concerns about potential electromagnetic field (EMF) exposure for pedestrians. International guidelines for human exposure have introduced absorbed power density (APD) and incident power density (IPD) as physical quantities for evaluating local exposure above 6 GHz. However, pedestrian exposure to automotive radars has been insufficiently investigated, particularly in vehicle–pedestrian interactions with radar operating while stationary. This study employed computational simulations and experimental measurements to evaluate the exposure from a 12 × 1 patch antenna array operating at 79 GHz. Exposure scenarios were analyzed using simplified geometric models and anatomically realistic human models at varying distances and equivalent isotropically radiated power (EIRP) levels. The results demonstrate a good agreement between the simulated and measured electric field distributions in both the near- and far-field regions. For continuous exposure, APD values obtained from anatomical models were consistently lower than those obtained from simplified geometries. At EIRPs of 26.7 dBm and 35.4 dBm, both APD and IPD remain within permissible limits across all distances. In contrast, the exposure at higher power levels (e.g., 55 dBm EIRP) exceeded the APD threshold. Nevertheless, evaluation using absorbed energy density, a metric for brief exposures, indicated compliance even when the human model was positioned directly adjacent to the vehicle surface. These findings provide critical insights into ensuring the conformity and design of next-generation automotive radar development.","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 4","pages":"793-803"},"PeriodicalIF":6.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-10DOI: 10.1109/JMW.2025.3579668
Isabella Lenz;Yu Rong;Adarsh A. Venkataramani;Daniel W. Bliss
This work presents a novel non-contact heart sound monitoring approach using millimeter-wave RADAR technology. The proposed system enables simultaneous heart sound acquisition from multiple subjects, offering a contactless and efficient alternative to traditional stethoscopes, which are limited by the need for direct contact and the inability to monitor multiple subjects concurrently. The RADAR-based heart sound system detects surface skin vibrations induced by the heart's mechanical motions through the chest cavity. It translates these mechanical displacements into time-frequency signals for heart sound analysis. The system employs a Frequency-Modulated Continuous-Wave RADAR with optimized parameters for heart sound recording. A complete RADAR signal processing chain is developed, incorporating automatic subject detection and localization using temporal features, spatial beamforming to separate signals from multiple subjects, and heart sound signal extraction. Experimental results demonstrate the system's capability to capture distinct heart sound signatures from up to three subjects simultaneously, with heart rates matching those obtained from reference digital stethoscopes. These findings highlight the potential of millimeter-wave RADAR technology for advanced biomedical sensing applications, enabling remote and simultaneous monitoring of multiple individuals in clinical and non-clinical environments.
{"title":"Multi-Subject Remote Heart Sound Monitoring Using mmWave MIMO RADAR","authors":"Isabella Lenz;Yu Rong;Adarsh A. Venkataramani;Daniel W. Bliss","doi":"10.1109/JMW.2025.3579668","DOIUrl":"https://doi.org/10.1109/JMW.2025.3579668","url":null,"abstract":"This work presents a novel non-contact heart sound monitoring approach using millimeter-wave RADAR technology. The proposed system enables simultaneous heart sound acquisition from multiple subjects, offering a contactless and efficient alternative to traditional stethoscopes, which are limited by the need for direct contact and the inability to monitor multiple subjects concurrently. The RADAR-based heart sound system detects surface skin vibrations induced by the heart's mechanical motions through the chest cavity. It translates these mechanical displacements into time-frequency signals for heart sound analysis. The system employs a Frequency-Modulated Continuous-Wave RADAR with optimized parameters for heart sound recording. A complete RADAR signal processing chain is developed, incorporating automatic subject detection and localization using temporal features, spatial beamforming to separate signals from multiple subjects, and heart sound signal extraction. Experimental results demonstrate the system's capability to capture distinct heart sound signatures from up to three subjects simultaneously, with heart rates matching those obtained from reference digital stethoscopes. These findings highlight the potential of millimeter-wave RADAR technology for advanced biomedical sensing applications, enabling remote and simultaneous monitoring of multiple individuals in clinical and non-clinical environments.","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 4","pages":"767-775"},"PeriodicalIF":6.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-10DOI: 10.1109/JMW.2025.3579905
{"title":"IEEE Microwave Theory and Technology Society Publication Information","authors":"","doi":"10.1109/JMW.2025.3579905","DOIUrl":"https://doi.org/10.1109/JMW.2025.3579905","url":null,"abstract":"","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 4","pages":"C2-C2"},"PeriodicalIF":6.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-10DOI: 10.1109/JMW.2025.3575723
Cory Hilton;Sheng Huang;Steve Bush;Faiz Sherman;Matt Barker;Aditya Deshpande;Steve Willeke;Jeffrey A. Nanzer
We present the design of narrowband radio-frequency harmonic tags and demonstrate their use in the classification of common motions of held objects using harmonic micro-Doppler signatures. Harmonic tags capture incident signals and retransmit at harmonic frequencies, making them easier to distinguish from clutter. We characterize the motion of tagged, held objects via the time-varying frequency shift of the harmonic signals (harmonic Doppler). With complex micromotions of held objects, the time-frequency response manifests complex micro-Doppler signatures that can be used to classify the motions. We describe the design of narrow-band harmonic tags at 2.4/4.8 GHz, supporting frequency scalability for multi-tag operation, and a harmonic radar system to transmit a 2.4 GHz continuous-wave signal and receive the scattered 4.8 GHz harmonic signal. Experiments were conducted to mimic four common motions of held objects from 35 subjects in a cluttered indoor environment. A 7-layer convolutional neural network (CNN) multi-class classifier was developed that obtained a real time classification accuracy of 94.24$%$, with a response time of 2 seconds per sample, and with a data processing latency of less than 0.5 seconds.
{"title":"Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network","authors":"Cory Hilton;Sheng Huang;Steve Bush;Faiz Sherman;Matt Barker;Aditya Deshpande;Steve Willeke;Jeffrey A. Nanzer","doi":"10.1109/JMW.2025.3575723","DOIUrl":"https://doi.org/10.1109/JMW.2025.3575723","url":null,"abstract":"We present the design of narrowband radio-frequency harmonic tags and demonstrate their use in the classification of common motions of held objects using harmonic micro-Doppler signatures. Harmonic tags capture incident signals and retransmit at harmonic frequencies, making them easier to distinguish from clutter. We characterize the motion of tagged, held objects via the time-varying frequency shift of the harmonic signals (harmonic Doppler). With complex micromotions of held objects, the time-frequency response manifests complex micro-Doppler signatures that can be used to classify the motions. We describe the design of narrow-band harmonic tags at 2.4/4.8 GHz, supporting frequency scalability for multi-tag operation, and a harmonic radar system to transmit a 2.4 GHz continuous-wave signal and receive the scattered 4.8 GHz harmonic signal. Experiments were conducted to mimic four common motions of held objects from 35 subjects in a cluttered indoor environment. A 7-layer convolutional neural network (CNN) multi-class classifier was developed that obtained a real time classification accuracy of 94.24<inline-formula><tex-math>$%$</tex-math></inline-formula>, with a response time of 2 seconds per sample, and with a data processing latency of less than 0.5 seconds.","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 4","pages":"882-891"},"PeriodicalIF":6.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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