antenna array modeling

Existing channel models struggle to accurately capture the impact of antenna configurations on signal propagation in reconfigurable antenna systems, often neglecting polarization effects or relying on oversimplified assumptions. This work proposes a general electromagnetic channel model based on spherical vector wave expansion (SVWE), which, for the first time, fully incorporates antenna position, orientation, and polarization effects, rendering it applicable to a wide range of reconfigurable antennas. The model is rigorously derived from electromagnetic field theory and validated against commercial simulation software, demonstrating excellent predictive accuracy. Experimental results further reveal that dynamically optimizing antenna orientation can enhance communication rates by up to 70% compared to fixed configurations.

Large intelligent surfaces (LIS) suffer significant receive-performance degradation under practical hardware impairments—including RF-chain distortions, power amplifier back-off, and AGC nonlinearities—yet existing analyses often assume ideal hardware. Method: We propose a joint antenna-and-multipanel selection framework tailored to hardware impairments. First, we model RF-chain distortion via a memoryless polynomial model and derive a closed-form expression for the signal-to-interference-plus-noise distortion ratio (SINDR) under maximum-ratio combining (MRC). We further quantify, for the first time, the performance penalty induced by the ideal-hardware assumption. Then, we design a low-complexity combinatorial optimization algorithm for hardware-aware selection. Contribution/Results: The framework reduces the number of impaired receive chains by over 40% via antenna selection; multipanel selection further improves the complexity–performance trade-off, enabling substantial LIS size reduction—up to 50% fewer elements—for equivalent performance. This work provides a scalable, system-level optimization solution for LIS deployment under realistic hardware constraints.

Antenna modeling traditionally relies heavily on expert knowledge and involves labor-intensive, iterative workflows, severely hindering design efficiency. To address this, we propose LEAM—a prompt-driven antenna modeling paradigm leveraging large language models (LLMs) without fine-tuning or training. LEAM enables end-to-end generation of parameterized electromagnetic models directly from heterogeneous inputs—including natural language specifications, antenna images, and technical text from patents or papers—using only carefully engineered multimodal prompts. The approach integrates LLM-based semantic understanding with domain-specific electromagnetic structural priors and interfaces with Antenna Toolbox as well as commercial simulators (CST Studio Suite and ANSYS HFSS) to export simulation-ready models. Evaluated on three canonical antenna types—Vivaldi, slotted patch, and monopole-slotted antennas—LEAM consistently produces correct, simulatable models within minutes, dramatically accelerating design iteration.

Existing reconfigurable electromagnetic structures (REMS)—including reconfigurable intelligent surfaces (RIS) and reconfigurable reflectarrays (RRAs)—suffer from a fundamental trade-off between computational efficiency and physical fidelity in modeling, forcing control algorithms to rely on oversimplified, inaccurate surrogate models. This work proposes a unified, physics-informed modeling framework that integrates circuit-theoretic descriptions with far-field electromagnetic interaction characterization. Leveraging only a single full-wave simulation, the framework enables rapid prediction of the complete far-field radiation response for arbitrary tunable element configurations. It rigorously satisfies Maxwell’s equations and consistently incorporates mutual coupling, polarization effects, dielectric/conductor losses, nonreciprocal responses, and thermal noise. The resulting model achieves accuracy comparable to full-wave simulation while accelerating computation by over two orders of magnitude. Furthermore, it enables the first real-time, high-fidelity multi-user beam and null synthesis algorithm capable of joint beamforming and null-steering optimization.

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 need for efficient modeling of complex electromagnetic far-field behavior in modern wireless systems, where precise control and reflection of electromagnetic waves are critical. Building upon Maxwell’s equations and integrating frequency-domain bandwidth modeling with finite-rank operator approximation theory, the work rigorously establishes—for the first time—that the far-field response of general antenna architectures possesses intrinsic finite complexity. It further demonstrates that the approximation error decays super-exponentially with increasing operator rank. These findings provide a foundational finite-parameter representation theory for antenna far fields, offering a rigorous theoretical basis for high-fidelity, computationally efficient digital electromagnetic simulations.

Conventional discrete array models struggle to accurately characterize the channel and signal properties of large-scale, high-density, high-frequency reconfigurable electromagnetic aperture systems. This work proposes a continuous-space modeling paradigm that unifies the representation of channels, signals, and beamformers as continuous fields and operators governed by Maxwell’s equations, thereby effecting a fundamental shift from discrete to continuous formulations. By integrating electromagnetics with information theory and leveraging wavenumber-domain analysis, functional analysis, and compressive sensing, the study reveals the intrinsic degrees of freedom and capacity limits of continuous apertures and develops finite-dimensional equivalent methods capable of handling infinite-dimensional problems. These contributions establish a theoretical foundation, provide essential analytical tools, and outline practical hardware implementation pathways for future wireless systems.

This work addresses the significant overhead in channel state information acquisition and feedback caused by the high-dimensional radiation patterns of massive MIMO and reconfigurable intelligent surfaces (RIS), which hinders efficient beam management. To overcome this, the authors propose a training-free, compressed representation model for radiation patterns. They introduce a novel 3D pattern modeling approach that combines low-order spherical harmonics with anisotropic Gaussian kernels, and for one-dimensional azimuth slices of RIS responses, they design a hybrid sparse representation using Fourier bases and 1D Gaussians. This method yields a hardware-aware, interpretable, and high-fidelity low-dimensional parameterization. Experiments on the AERPAW platform and a public RIS dataset demonstrate reconstruction mean squared errors reduced to 1/2.8 and 1/10.4 of baseline methods, respectively. Simulations further show a 12.65% average uplink throughput gain under a fixed uplink budget.

This work addresses the lack of physical consistency in commonly adopted simplified models for near-field multi-antenna communications, whose applicability in real systems remains questionable. For the first time, a physically consistent near-field reference model grounded in electromagnetic field theory is established, enabling a systematic evaluation of the accuracy of representative simplified models. The results reveal that while these simplified models can support basic beam focusing, they exhibit significant deviations in sidelobe structure and frequency response characteristics, exposing their fundamental limitations in complex scenarios. This study provides a reliable benchmark for near-field modeling and clearly delineates the operational boundaries within which existing approaches remain valid.

This work addresses the common oversight in existing reconfigurable antenna systems that neglect mutual coupling, thereby missing its potential to enhance channel capacity. For the first time, mutual coupling is treated not as an impairment but as a designable mechanism for performance improvement. The paper develops a unified joint optimization framework for both narrowband and wideband MIMO systems, actively exploiting mutual coupling–induced superdirectivity through coordinated optimization of antenna positions and beamforming. To this end, it introduces a circuit-theory–based mutual coupling matrix design, a unified antenna placement strategy suitable for wideband scenarios, and integrates block coordinate ascent, a Sylvester equation–based trust-region algorithm, and subcarrier joint optimization techniques. Extensive simulations under diverse channel conditions demonstrate that the proposed approach significantly improves system capacity and sum rate, confirming the effectiveness and superiority of deliberately harnessing mutual coupling.