Applied Computational Electromagnetics Society Journal (ACES)

Digital Twin Assisted Self-Interference Cancellation for Wideband Full Duplex Radios

Md Rakibur Rahman, Satheesh Bojja Venkatakrishnan, John L. Volakis — Thu, 30 Jul 2026 00:00:00 +0200

Achieving high self-interference cancellation (SIC) in broadband in-band full-duplex (IBFD) radios requires a multi-stage passive architecture using real-world components. A fundamental challenge in such systems is that practical components (cables, connectors, splitters) deviate from their ideal behavior due to fabrication tolerances and parasitic effects. As such, they introduce phase and amplitude mismatches that severely degrade isolation performance. Even deviations of a few degrees in phase or a fraction of a dB in amplitude can reduce achievable cancellation by 10 dB or more.

To bridge this gap, we develop a digital twin framework that models the idealized system behavior and enables compensation through de-embedding of the fabricated hardware. Experimental results demonstrate that the proposed method improves the average isolation by 15 dB across a 1 GHz bandwidth within the X-band. Overall, a peak IBFD isolation of 65 dB is achieved, thus validating the effectiveness of the digital twin framework.

Three-Port Common Aperture Metasurface Antenna Designed Using Characteristic Mode Analysis

Z. N. Jiang, L. Wu, B. Z. Sun, L. L. Yu — Thu, 30 Jul 2026 00:00:00 +0200

In this paper, a three-port common aperture antenna based on metasurface (MTS) is presented. The omnidirectional radiation pattern of the two frequency bands is excited through the coupling of the central coaxial cable and the slit feed, respectively. The annular microstrip structure is introduced into the multi-layer structure design to generate wide-beam radiation along the +Z-axis. Characteristic modal analysis (CMA) is used to adjust the shape and size of the MTS patch to increase the bandwidth of the omnidirectional mode. The optimized antenna size is 1.25λ×1.25λ×0.1λ_min. Simulation and measurement results show that the omnidirectional radiation characteristics can be realized in dual-band impedance bandwidths of 2.92–3.08 GHz and 4.11–4.69 GHz, respectively. In the 1.74–1.87 GHz band, the half-power beamwidth (HPBW) of the E and H planes is at least 100∘ and 80∘, respectively. The antenna achieves the performance of multi-band and wide-angle radiation while saving space volume as much as possible.

A Bear-Shaped UWB MIMO Antenna Based on Characteristic Mode Analysis

Xiaoyan Wei, Zhonggen Wang, Wenyan Nie, Chenlu Li, Zhengting Zhang — Thu, 30 Jul 2026 00:00:00 +0200

This paper proposes a compact four-port ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna with bear-shaped patches based on characteristic mode analysis (CMA). The antenna measures 62×62×1mm3, comprising orthogonally arranged bear-shaped radiating elements on the top layer, a cross-shaped hollowed-out decoupling structure on the bottom layer, and defected ground structures (DGS) with corner cuts and grooves at the four corners. Notably, the bear-shaped radiating elements are not just a morphological innovation; compared with traditional rectangular/circular elements, their irregular outline enriches equivalent current path diversity and increases excited resonant modes. Each antenna is optimized with etched slots, corner cuts and a T-shaped feeder, ensuring simultaneous excitation of five characteristic modes for UWB performance. For the MIMO configuration, mutual coupling is suppressed via orthogonal arrangement, the decoupling structure and DGS, with isolation over 20 dB. Simulations and measurements confirm the antenna operates in the 3.36–21.91 GHz band, featuring S11 < −10 dB and ECC < 0.005 across the band, a diversity gain approaching 9.9999 dB, 75–95% radiation efficiency, 7 dBi peak gain and a group delay less than 1 ns. This design outperforms existing designs in bandwidth, isolation and miniaturization, suited for advanced wireless communication scenarios requiring wide bandwidth, high isolation and a compact form factor.

A Deep Learning and NSGA-II Based Multi-Objective Optimization Framework for SAR-Aware AMC-Backed Wearable Antenna Design

Muthukumaravel K, Suganthi Santhanam — Thu, 30 Jul 2026 00:00:00 +0200

This paper presents a SAR-aware machine-learning framework for optimizing an AMC-backed triple-band wearable antenna based on a two-iteration Sierpinski carpet radiator operating at 1.82, 2.42, and 5.00 GHz. A 600-sample full-wave CST dataset varies feed length, AMC-to-antenna gap, fractal scale, and AMC inter-cell gap. A multi-task DNN maps four normalized inputs to twelve outputs: gain, resonance-frequency deviation, SAR1g, and SAR10g across all bands. NSGA-II then identifies Pareto-optimal gain–SAR trade-offs, while SHAP indicates feed length and AMC gap dominate SAR. The optimized design achieves gains of 4.12/4.25/4.38 dBi and SAR of 0.082/0.075 W/kg, satisfying IEEE C95.1 and ICNIRP limits and improving over 2.6–2.9 dBi standalone gain values.

Deep Learning Enhanced Hybrid BEM-rMLNCA Method for Efficient Power Cable Crosstalk Prediction

Abdul Mueed, Weiqiang Tang, Rui Ma — Thu, 30 Jul 2026 00:00:00 +0200

This paper presents a deep learning-assisted hybrid Boundary Element Method (BEM)-recompressed Multilevel Nested Cross Approximation (rMLNCA) algorithm for efficient and accurate crosstalk prediction. This hybrid approach aims to combine the physics-based precision of BEM with the compression efficiency of rMLNCA and the nonlinear learning capability of deep neural networks (DNN) for high-fidelity modeling with reduced computational requirements. Under this proposed method, three parameters are predicted for crosstalk: Near-End Crosstalk (NEXT), Far-End Crosstalk (FEXT), and Common-Mode Current (CMC) measurement. The impedance and coupling parameters are computed using BEM, while rMLNCA decomposes and recompresses the far-field matrices into low-rank admissible blocks, reducing both memory and time significantly. It improves predictive accuracy further by learning nonlinear relationships among geometric, material, and frequency-dependent features using an optimized deep neural network architecture. The suggested model has high predictive consistency with the 5-fold cross-validation R² of 0.9736±0.0053. Final evaluation results showed that there is strong accuracy, as depicted by R² and MAPE values for NEXT, FEXT, and CMC of 0.9707/15.10%, 0.9602/11.11%, and 0.9750/8.19%, respectively, validating generalization performance.

An Efficient Multi-GPU Accelerated IEDG-CBFM-SMWA Method for Electromagnetic Scattering Analysis

Ziang Shen, Xinlei Chen, Zhuo Li — Thu, 30 Jul 2026 00:00:00 +0200

This paper presents a multiple graphics processing units (GPUs) accelerated direct solver for the method of moments (MoM). First, we integrate the characteristic basis function method-Sherman-Morrison-Woodbury formula-based algorithm (CBFM-SMWA) and the integral equation discontinuous Galerkin (IEDG) method. The IEDG can enforce current continuity across nonconformal meshes by employing hybrid RWG/half-RWG basis functions. Second, we develop a SMWA-centric multi-GPU parallelization strategy that fully exploits the intrinsic parallelism in compression and solution of hierarchical matrices, while CBFM serves to reduce the number of unknowns. The proposed multi-GPU IEDG-CBFM-SMWA achieves substantial computational acceleration over its CPU implementation while overcoming the memory limitations of conventional GPU-accelerated MoM. Numerical examples demonstrate that the proposed method offers excellent scalability across multiple GPUs, significant performance advantages and reliable accuracy for solving electrically large scattering problems.

Ant Colony Algorithm Optimized Method for Electromagnetic Interference Suppression of Power Line Inspection UAVs

Jian Chen, Yuxuan Huang, Ziheng Wan — Thu, 30 Jul 2026 00:00:00 +0200

Power inspection drones face severe electromagnetic interference issues in complex electromagnetic environments. Methods such as wavelet transform, matched filtering, and traditional support vector machines (SVM) have been applied in the field of interference suppression, however, the transient characteristics of interference are highly random and bursty. Existing methods have significant deficiencies in extracting key information in the time-frequency domain, making it difficult to accurately characterize interference features. This leads to large deviations in the peak values of frequency-domain amplitude characteristics and fails to achieve the desired suppression performance. To effectively suppress electromagnetic interference, an ant colony algorithm-optimized electromagnetic interference suppression method for power inspection drones is proposed. Wavelet decomposition transform is used to optimize the signal waveform of power inspection drones, thereby improving signal quality. By combining discrete Fourier transform processing and matched filtering transform, transient time-frequency domain features of interference are extracted based on the optimized drone signal waveform. Using the transient time-frequency domain features, a SVM trained with a linear kernel function is employed to detect high-density integrated electromagnetic interference. A filter is adopted to suppress electromagnetic interference by adjusting its coefficients, and the ant colony algorithm is utilized to adaptively optimize these coefficients, achieving high-density integrated electromagnetic interference suppression for power inspection drones. Experimental results show that the optimized signal waveform is smoother and more stable. The error between the frequency-domain amplitude characteristic peak of electromagnetic interference signals extracted by this method and the actual interference peak is less than 5 dB. After applying the proposed suppression method, the electromagnetic interference intensity is reduced to below 1W/cm², and the signal-to-noise ratio (SNR) of the suppressed signal is relatively high.

A Hybrid Triangle–Voxel Model with Local Voxelization for Resolving Diffraction Edge Deficiency in Ray Tracing

Thu, 30 Jul 2026 00:00:00 +0200

Accurate diffraction modeling is essential for ray-tracing-based wireless propagation prediction. The geometric representation of objects in the simulation scene greatly affects diffraction prediction accuracy. Conventional triangular meshes lack the explicit edge information required for diffraction modeling, while global voxelization introduces geometric distortion and substantial computational overhead. This paper presents a hybrid triangle–voxel model that performs local voxelization only within potential diffraction regions (PDR) determined by the first Fresnel zone. The proposed method reconstructs diffraction paths that cannot be captured by pure triangular meshes while preserving triangular facets for direct and reflected rays. Compared with a commercial ray-tracing tool, it achieves an average ray–surface intersection error of 246.4 mm and average path loss, delay, and phase errors of 0.35 dB, 0.12 ns, and 42.0^∘, respectively. It also reduces the voxel count and computation time by 60% and 25%. The results show that the proposed model balances diffraction-path recoverability, geometric fidelity, and computational efficiency.

Numerical Modeling and Uncertainty Analysis of Downhole Low-Frequency Electromagnetic Transmission

Dongxiao Pang, Huiyan Hou, Jinjun Bai, Xiong Han — Thu, 30 Jul 2026 00:00:00 +0200

As oil and gas resource development extends into increasingly complex geological conditions, reliable downhole signal transmission has become a critical challenge in drilling and measurement-while-drilling (MWD) operations. In engineering practice, downhole low-frequency electromagnetic wave (LF-EM) transmission is widely used as an important means of downhole data telemetry because of its low dependence on drilling fluid. However, uncertainties in formation properties and fluctuations in geological parameters can significantly affect electromagnetic signal transmission, making it difficult for surface systems to stably receive downhole information in practical applications. To address this issue, a transmission channel model for downhole LF-EM is established based on FEKO. First, sensitivity analysis is performed to identify the key uncertainty parameters that have the most significant influence on signal attenuation. Then, the Kriging method and the Stochastic Collocation Method (SCM) are employed to conduct uncertainty analysis and to obtain the probability density function (PDF) of the received magnetic-field strength within the parameter fluctuation range. To compare the reliability and applicability of the two methods, Monte Carlo Method (MCM) simulation is adopted as the benchmark, and the Mean Equivalent Area Method (MEAM) is introduced for comprehensive evaluation. Results show that the SCM exhibits overall better fitting accuracy and computational stability than the Kriging method, making it more suitable for probabilistic prediction and risk assessment of downhole low-frequency electromagnetic transmission.

Frequency-Selective Polarization Converter with Ultra-Wideband Linear-to-Circular Conversion and High Selectivity Based on Stair-Case Shape

Kun Liao, Jie Shu, Ruquan Wen, Renxia Ning, Zhiming Liu — Thu, 30 Jul 2026 00:00:00 +0200

In this paper, an ultra-wideband and high-selectivity reflective polarization converter is presented, which is based on a simple stair-case shape. The simulation results indicate that the polarization converter achieves ultra-wideband linear-to-circular polarization conversion under linearly polarized incident wave. Its 3 dB axial ratio frequency band is 5.47–16.15 GHz, and its relative bandwidth is 98.8%; besides, linear-to-linear polarization conversion is realized on both sides of the circular polarization band from 4.57 to 5.30 GHz and 16.28 to 18.11 GHz when S₁₁<−10 dB. Therefore, multiple polarization functions are realized using a single simple structure. Moreover, the physical mechanism and surface current distributions of the polarization converter are analyzed. In addition, the sample is processed and tested, verifying the feasibility of our idea.