multi-port coupling model construction

Conventional approaches to converting among multi-mode microwave network parameters (e.g., S, T, ABCD, Z, Y, h) rely on case-specific algebraic derivations, lacking a unified, generalizable framework. Method: This paper proposes the first algebraic framework grounded in state-vector spaces and basis transformations, modeling parameter conversion as a linear basis-change problem—thereby eliminating ad hoc derivations and enabling closed-form conversion formulas between any two parameter sets. Contribution/Results: The framework is theoretically self-consistent, inherently extensible to arbitrary parameter types and mode orders, and rigorously validated via high-fidelity electromagnetic simulations. It achieves both high accuracy and computational efficiency, providing a unified, robust foundation for modeling, simulation, and design of high-frequency and millimeter-wave multi-mode circuits.

This work addresses the lack of an effective Q-factor-based bandwidth estimation method for multiport antennas, which hinders accurate characterization of their frequency response. The study extends the classical single-port Q-factor theory to multiport scenarios by introducing an equivalent electromagnetic energy storage matrix associated with antenna ports. By integrating this matrix with the total effective reflection coefficient and multiport network parameters, the authors derive a closed-form analytical expression directly applicable to bandwidth prediction. The proposed framework elucidates how feed configurations and matching networks influence achievable bandwidth. Validation on both a dual-dipole array and an electrically large patch antenna array demonstrates the model’s high accuracy in evaluating bandwidth from a single-frequency-point measurement.

This work addresses the modeling challenge of hybrid electromagnetic systems comprising multiple scatterers and antennas. We propose a unified generalized scattering matrix (GS-matrix) synthesis method based on the vector spherical wave function addition theorem. The approach enables efficient, purely matrix-based coupling of individual components’ local GS-matrices—without field-level integrals or iterative solvers. We establish, for the first time, a general GS-matrix domain decomposition theory applicable to arbitrary antenna–scattering hybrid systems, subsuming classical multi-scattering and antenna array models as special cases. The formulation inherently accommodates dynamic geometric transformations, such as structural rotations. Comprehensive validation across diverse scenarios confirms high accuracy and broad compatibility with various component types and configurations. The method significantly enhances modeling efficiency and flexibility for complex electromagnetic systems, enabling rapid, scalable, and physics-preserving analysis of integrated antenna–scattering architectures.

This paper addresses the challenge of multiscale modeling in macroeconomic systems by proposing a novel economic modeling framework grounded in multiport network theory. Methodologically, economic agents are mapped to ports, commodity flows are analogized to electrical currents, and incentive mechanisms to voltages; macrodynamic behavior emerges rigorously from micro-level interactions via port coupling. For the first time, the circuit-theoretic multiport paradigm is systematically imported into economics, and an analytically tractable, scalable cross-scale dynamic model is constructed using LTSpice simulation. The key contributions are: (1) establishing a theoretically consistent micro–macro bridge; (2) validating the framework across hierarchical scales—from Robinson Crusoe–style isolated economies to full national economies; and (3) demonstrating that macroeconomic phenomena can be strictly derived from microscopic port interactions. This work provides a new paradigm for mechanistic interpretation and policy simulation in complex economic systems.

Conventional Rician channel models for ultra-wideband (UWB) MIMO communications suffer from physical inconsistency and limited bandwidth validity due to unmodeled antenna mutual coupling. Method: This paper proposes the first physically consistent wideband Rician channel modeling framework, embedding circuit theory into the standard MIMO channel representation. It jointly models antenna port impedances, mutual coupling networks, and propagation paths, explicitly characterizing how mutual coupling distorts the amplitude and phase of the line-of-sight (LOS) component—and its frequency dependence. Contributions/Results: First, it reveals that tight coupling reduces spatial correlation at lower frequencies. Second, it quantifies mutual-coupling-induced beamforming performance deviation. Third, it demonstrates a significant bandwidth broadening effect enabled by the new model. The framework provides an interpretable, scalable, physics-based foundation for UWB MIMO system design, channel estimation, and beam optimization.

This study addresses the neglect of antenna mutual coupling in existing metasurface-inspired large intelligent reflecting surface (MiLAC)-assisted MIMO research, which leads to model inaccuracies. Building upon multiport network theory, this work develops a physically consistent end-to-end MiLAC-MIMO model and proposes a mutual coupling-aware optimization framework to maximize received power. It is the first to reveal that mutual coupling inherently provides an averaging gain in MiLAC systems, demonstrating that such systems achieve performance equivalent to digital architectures equipped with impedance matching networks—yet require fewer RF chains—and consistently outperform configurations without matching networks. By leveraging convex optimization and closed-form solutions, the paper derives three analytical performance bounds, which are validated through extensive simulations.

This study addresses the accurate characterization of high-frequency performance in dual-port bridged-T networks for microstrip high-pass filters. By establishing a parametric model based on transmission and scattering matrices, the authors derive the S-parameters and analyze their magnitude and phase responses. It is found that when the inductances satisfy \( L_1 = L_2 \), the \( S_{11} \) transfer function contains only odd-order terms, enabling structural simplification and yielding a high-performance high-pass response. Leveraging frequency normalization and Keysight ADS electromagnetic simulation, a filter with a 1 GHz cutoff frequency is designed, demonstrating excellent roll-off characteristics at the passband edge, with slopes of \( S_{11} \) and \( S_{21} \) reaching −30 dB/GHz and −32 dB/GHz, respectively.

This study addresses the significant degradation of beamforming performance in continuous-aperture arrays caused by electromagnetic mutual coupling, a challenge exacerbated by the high computational complexity and low solution efficiency of existing methods when incorporating accurate coupling models. Building upon a physically consistent mutual coupling model, the authors formulate beamforming design as a functional optimization problem and characterize its optimality conditions via a Fredholm integral equation. To solve this efficiently, they propose two strategies: a coordinate-transform-based kernel approximation that preserves operator structure while reducing discretization dimensionality, and a direct solver combining Nyström discretization with LU decomposition, augmented by offline factorization to enable stable and scalable large-scale optimization. Experiments demonstrate that the proposed approaches substantially reduce computational cost while maintaining accuracy, with the LU-based solver exhibiting exceptional efficiency and scalability in large-scale scenarios.

This study addresses the challenge of load prediction in multi-port scatterers, where high dimensionality and the complex nonlinear relationship between impedance and scattering responses hinder accurate modeling. To overcome this, the authors propose a two-stage clustering-regression framework: first clustering S-parameter data and then performing regression within each cluster. The work introduces the novel “Reality-Unified Index” (RUI) to holistically evaluate model performance under conflicting multi-objective criteria and systematically optimizes the combination of clustering and regression techniques. Experimental results demonstrate that the proposed architecture reduces RMSE by 46% when implemented with gradient boosting models. Furthermore, RUI-based validation identifies K-means clustering paired with k-nearest neighbors regression as the optimal configuration, significantly enhancing both prediction accuracy and generalization capability.

This work addresses the challenge of constructing high-fidelity surrogate models for high-dimensional electromagnetic simulations under limited computational budgets, where strong parameter coupling and high evaluation costs hinder conventional approaches. The authors systematically investigate low-rank tensor function representations—including Tucker, tensor train (TT), and tensor ring (TR)—and propose PLRNet, a novel framework that leverages learnable pairwise interaction factors and compact coordinate embeddings to effectively capture nonlinear couplings among high-dimensional variables. Experimental results on representative electromagnetic surrogate modeling tasks demonstrate that PLRNet significantly outperforms existing methods, achieving superior accuracy, robustness, and parameter efficiency in high-dimensional design spaces, while also exhibiting enhanced optimization stability.