PIER M | PIER Journals

2025-12-16 Latest Published

Auto-Calibration of Near-Field Microwave Measurements for Complex Permittivity Estimation Download Full Article View Full Article

By Andrei Ludvig-Osipov Simon Stenmark Thomas Rylander Tomas McKelvey

Progress In Electromagnetics Research M, Vol. 136, 46-56, 2025

doi:10.2528/PIERM25090302

Abstract

This paper presents a numerical study of a novel method for auto-calibration of scatteringparameter measurements in a near-field microwave sensor system. The here proposed method is applied to estimation of the average complex permittivity in a measurement domain from the scattering parameters, corrupted by gain uncertainties in the measurement instruments. Simultaneously with the average complex permittivity, the gain uncertainties are also estimated. The characteristic property of the proposed method is that no simplified mathematical model of the measurement domain is assumed, and instead a set of a-priori calibrated measurements is used. Numerical studies demonstrate the performance of the method in noiseless and noisy settings with and without nuisance stochastic perturbations in the measurement domain. An approach to compensate for the stochastic perturbations in the measurement domain permittivity is proposed, and it demonstrates an improved performance of the method in numerical examinations.

2025-11-17

PIER M

Vol. 136, 33-45, 2025

doi:10.2528/PIERM25082502

download: 122

Enhanced Low-Resolution Contrast Operator Using Neural Networks for E-Polarized EM Scattering Problems

Daan van den Hof, Martijn Constant van Beurden and Roeland J. Dilz

Coarse discretization introduces significant errors in the solution of scattering problems, in part due to discretization errors in the contrast operator. We present a procedure for the automatic construction of a modified contrast operator for electromagnetic scattering problems by using trainable neural networks to represent a modified contrast operator. We achieve a higher accuracy on a coarse discretization while still keeping computation time down compared to a fine discretization. By using synthetic data from a full-wave Maxwell solver to train the network for one-dimensional slab scatterers and two-dimensional polygonal scatterers, we are able to use the techniques found in deep learning to improve accuracy in coarse-grid forward scattering problems.

Enhanced Low-resolution Contrast Operator Using Neural Networks for E-polarized EM Scattering Problems

2025-11-11

PIER M

Vol. 136, 22-32, 2025

doi:10.2528/PIERM25031101

download: 91

Parameter Enhancement of Vivaldi Slot 1×2 Array MIMO Antenna Using AMC

Ameet Mukund Mehta, Shankar B. Deosarkar, Anil Bapusa Nandgaonkar and Avinash R. Vaidya

A wide band, high gain 1 × 2 array Vivaldi shaped slot Substrate Integrated Waveguide (SIW) Multiple Input Multiple Output (MIMO) antenna with square shaped periodic Artificial Magnetic Conductor (AMC) placed beneath the antenna for applications in X band is presented. A two-port MIMO antenna backed by AMC patches is designed and realized for enhanced gain and bandwidth. The single antenna 1 × 2 array has electrical dimensions of 1.57λ_r × 1.13λ_r × 0.027λ_r. The designed antenna structure has bandwidth of 1.39 GHz (8.79 GHz-10.18 GHz) with a percentage bandwidth of 14.65% and Gain of 11.67 dBi. The edge to edge distance between the MIMO antenna elements is 5 mm (λ_r/4). The periodic AMC patches improve vital MIMO antenna performance metrics like Isolation, Envelope Correlation Coefficient (ECC), Diversity Gain (DG), Channel Capacity Loss (CCL) and radiation pattern. The unit cell analysis of periodic square AMC patch and a polynomial regression model to find the best goodness of fit for Gain-Bandwidth product versus square AMC patch size is studied. Antenna gain variation seen over the complete bandwidth is < 1 dBi which makes it a flat gain response antenna. The proposed high-gain, wide-band 1 × 2 Vivaldi-slot SIW MIMO antenna with AMC is suitable for X-band radar, point-to-point high-throughput wireless links, and compact platform communication systems requiring robust diversity performance.

Parameter Enhancement of Vivaldi Slot 1×2 Array MIMO Antenna Using AMC

2025-11-03

PIER M

Vol. 136, 13-21, 2025

doi:10.2528/PIERM25071106

download: 107

Selective Signal Transmission and Crosstalk Suppression Based on Double-Layer RFID Tags

Peiying Lin, Jiangtao Huangfu, Xixi Wang, Dana Oprisan and Yanbin Yang

This paper presents a passive, structure-based approach for selective signal transmission and crosstalk suppression in dense radio frequency identification (RFID) tag environments. The proposed method employs a mechanically reconfigurable double-layer tag design based on the mirror-antenna principle, which enables dynamic switching between transmission and shielding modes by adjusting the interlayer spacing. Simulation results demonstrate pronounced differences in the reflection characteristics and radiation intensity of the tag under the two operating modes at 915 MHz. Experimental validation further confirms the effectiveness of the system in mitigating interference and ensuring reliable tag identification in multi-tag scenarios. The design is compact, energy-efficient, and cost-effective, supporting scalable applications in smart retail and automated inventory management.

Selective Signal Transmission and Crosstalk Suppression Based on Double-layer RFID Tags

2025-10-30

PIER M

Vol. 136, 1-12, 2025

doi:10.2528/PIERM25071303

download: 195

Coin-Sized Dual-Band Millimeter-Wave (mmWave) Antenna with Machine-Learning-Guided Impedance Prediction

Ahmed Jamal Abdullah Al-Gburi

This study suggests a coin-sized (10 × 8 × 0.64 mm³) millimetre-wave antenna that simultaneously resonates at 28 GHz and 38 GHz and is supported by a machine-learning surrogate for near-instant impedance evaluation. Realised on Rogers 6010 LM laminate (ε_r = 10.2), the radiator maintains |S₁₁| ≤ -10 dB across 26.5-29.9 GHz and 37.2-39.7 GHz while providing peak gains of 3.8 dBi and 4.1 dBi in the lower and upper bands, respectively. A design-of-experiments sweep, comprising 330 full-wave simulations, generated the training corpus for a random-forest regression model. The surrogate predicts frequency-resolved |S₁₁| with a mean-absolute error below 0.7 dB and coefficients of determination of 0.93 at 28 GHz and 0.84 at 38 GHz. The evaluation time is reduced from approximately 155 s per full-wave electromagnetic simulation to 0.1 s per surrogate query, enabling real-time design exploration. Eight-fold cross-validation confirms model stability, while feature-importance analysis identifies the geometric parameters most influential to dual-band matching. The learning-guided workflow therefore offers a fast and reliable alternative to exhaustive simulation, accelerating the optimisation of compact mmWave antennas for instrumentation, sensing, and future front-end modules.