This work presents a preliminary full-wave simulation analysis of a reverberation chamber (RC) loaded with a large enclosure that includes an aperture and internal lossy materials. The study investigates how the presence of a large structure and changes in its aperture size and internal absorbers affect key chamber characteristics such as the Rician K-factor, the scattering parameter S21, and the Quality (Q) factor. A 1.5 mx1.2mx1m metallic structure is modeled inside a large RC using the finite-difference time-domain method. Results for this configuration indicate that due to the presence of the metallic structure, the K-factor exhibits narrowband frequency spikes, whereas changes in the S21 and the Q-factor are more consistent across the analyzed frequency range. These preliminary findings will be extended in future work to include additional enclosure geometries, a wider range of aperture configurations, and experimental validation.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology in wireless communications, offering dynamic control over electromagnetic propagation to face challenges such as latency, interference, and multipath fading. However, optimizing RIS configurations in real time remains computationally prohibitive due to the exponential growth of the solution space with the number of elements. In this work, we explore quantum-based RIS optimization using the quantum approximate optimization algorithm (QAOA) within a model-driven framework based on the Sherrington–Kirkpatrick (SK) Hamiltonian. This formulation exploits superposition, entanglement, and quantum interference to enable parallel exploration of the configuration space, while avoiding the need for large training datasets and minimizing the risk of overfitting. To validate the method, we adopt a one-to-one mapping between RIS elements and qubits, in order to test the effectiveness of QAOA combined with SK model for RIS optimization. Our results reveal that barren plateaus—regions of vanishing gradient—emerge around 12–14 qubits, marking a fundamental scalability barrier under direct encoding. This diagnostic insight provides a baseline for future improvements and underscores the need for more scalable encodings and advanced training strategies to enable efficient RIS optimization in practical, large-scale wireless environments.

This work presents a quantum-computational framework for simulating electromagnetic wave propagation based on the Riemann-Silberstein formulation of Maxwell's equations. By combining the electric and magnetic fields into a single complex vector field, Maxwell's equations are recast into a quantum dynamics equation by a Hermitian Hamiltonian evolution operator. The spatial derivatives in this Hamiltonian are discretized using a finite-difference scheme, and decomposed into tensor products of Pauli matrices followed by Trotter-Suzuki decomposition enabling the system evolution to be computed on quantum hardware. The proposed method scales logaritmically with the number of qubits to represent N spatial points, providing an exponential memory advantage over classical solvers. To validate the approach, the TM mode formulation is applied to two representative geometries-L-shaped and cross-shaped domains-under perfect electric conductor boundary conditions. Numerical results confirm that the quantum formulation reproduces EM field propagation and reflection phenomena highlighting the method's potential for scalable quantum simulations in computational electromagnetics and photonics.

This study presents a comprehensive full-wave numerical dosimetry approach for large-scale rodent bioassays in Reverberation Chambers (RCs), improving upon prior methods that rely on idealized Plane Wave (PW) superposition. A "digital twin" of the Universit & agrave; Politecnica delle Marche RC was implemented using Transmission-Line Matrix and Finite Element Method solvers to characterize exposure homogeneity across rodent cages at 900 MHz. Unlike PW-superposition models, loaded-RC simulations account for realistic experimental constraints, such as intruding water-supply metal piping, specific antenna designs and placements, and actual mode stirrers. The loaded-RC electromagnetic characteristics were investigated by analyzing the field impedance ratios, yielding discrimination criteria for the probe location and type. Cage-wise instantaneous and ensemble-averaged whole-body specific absorption rate (wbSAR) distributions were evaluated using postured homogeneous rat models. Furthermore, investigations into exposure imbalance mitigation strategies demonstrated significant benefits when spinning cage assemblies. An analysis of mass-dependent exposures revealed a notably weaker correlation between body mass and wbSAR, as well as a much larger wbSAR cage dependence, compared to earlier PW-based predictions, further highlighting the necessity of realistic RC modeling for reliable rodent bioassay exposure design.

Reverberation chambers (RC) have been widely used to perform Electromagnetic Compatibility (EMC) testing due to their statistically uniform, isotropic, and well-scattered fields. Recently, because they can emulate realistic multipath propagation environments and provide repeatable test environments, their use in wireless device testing has increased rapidly. However, as the operating frequencies of wireless devices increase, it becomes essential to study and characterize RCs at these frequencies. This paper presents the characterization of a 60 GHz RC for millimeter-wave (mmWave) wireless testing through key metrics such as the quality factor (Q) and the Rician K-Factor, highlighting the differences with respect to lower frequency ranges. The testing on a real device confirms the results obtained during chamber characterization.

In this paper, a comprehensive statistical analysis of the Reverberation Chamber with random boundaries and a mechanical stirrer is performed to evaluate the field correlation for independent positions achieved from a stirred electromagnetic field. The analysis of the reverberation chamber is conducted through numerical simulation by using a custom FDTD solver. The reverberant environment is modeled with two monopole antennas along with $N_{s} 64$ random boundaries and stirrer positions. The degree of independence among the field samples is quantified through the spatial correlation matrix, while the Kolmogorov-Smirnov (KS) statistics provides an additional measure of statistical consistency. The use of randomized boundaries enables a well-stirred field across the chamber, and even though a residual unstirred energy may occur at lower frequencies due to the limited number of resonant modes, proper matching of the chamber size to the working frequency ensures effective stirring. The addition of a mechanical stirrer further enhances field uniformity and increases the number of independent positions even for a small-volume Reverberation Chamber, providing insights for efficient chamber design and characterization.

Reconfigurable intelligent surface (RIS) optimization is a high-dimensional combinatorial problem, where classical methods face significant scalability challenges. The quantum approximate optimization algorithm (QAOA) offers a promising alternative by encoding the optimization problem into a quantum Hamiltonian and finding its ground state through a parameterized quantum circuit, known as the ansatz [1]. In the quantum framework, the RIS optimization problem is mapped onto the Sherrington-Kirkpatrick (SK) Hamiltonian, which models the transfer function between the transmitting antenna (TX) and the receiver (RX) [2], [3] as follows:

We investigated the propagation condition in a particular environment, such as an area covered by water or mud. In particular, we analyzed the effect on signal propagation in a fifth-generation (5G) wireless communication system in that scenario. The experiments were carried out in a laboratory inside a reverberation chamber that emulates a complex propagation environment, equipped with a 5G base station connected to the TIM live network. We measured: 1) the permittivity of the medium under investigation; 2) the effects of the presence of such mixtures within the propagation environment to test the 5G system by checking the key performance indicators. The chosen situations emulate the radio channel propagation in a hostile scenario such as during a flood, to evaluate the performance of a 5G base station. For the characterization of the medium, we considered its physical properties such as the permittivity, conductivity, and reflection coefficients. Moreover, to assess radio performance of the 5G system, we report the following key performance indicators: RSRP, SINR, CQI, MCS and BLER. Experimental results show that water creates a greater multipath than mud, confirmed by direct measurements of permittivity and conductivity. Furthermore, experiments conducted in the laboratory reveal the same behavior of the propagation scenario in a realistic flood such as the lowering of about 3 dB in terms of RSRP between the normal situation and the flooded scenario.

Wireless communication systems play a pivotal role in modern society, yet they face significant challenges such as latency and multipath fading. Reconfigurable intelligent surface (RIS) is emerged as a promising solution to manipulate electromagnetic waves to enhance transmission quality, although their optimization presents several limitations. In this study, we propose a hybrid approach utilizing the quantum approximate optimization algorithm (QAOA) to effectively configure RIS in multipath environments, addressing the shortcomings of classical methods. The computational model trained by the Sherrington- Kirkpatrick Hamiltonian, demonstrates high accuracy in identifying optimal RIS configurations across various scenarios, without running optimization at each condition. However, the analysis of barren plateaus reveals that the cost function gradient diminishes exponentially as the number of cells increases, making hard the training for large-scale systems. To mitigate these issues, we conclude by suggesting some potential strategies for future research aimed at enhancing RIS performance in practical applications.

The advent of noisy intermediate-scale quantum (NISQ) systems signifies an important stage in quantum computing development. Despite the constraints due to their limited qubit numbers and noise susceptibility, NISQ devices exhibit substantial potential to tackle complex computational challenges via hybrid classical-quantum algorithms. Among the various hybrid algorithms, variational quantum algorithms (VQAs) are gaining increasing attention due to their ability to solve highly complex, large-scale problems where classical algorithms fail. In particular, the variational quantum eigensolver (VQE) shows its potential in calculating the energies and ground states of large systems, where the complexity of solving such problems grows exponentially and becomes intractable for classical computers. At this regard, the aim of this paper is to extend the use of VQE for solving circular waveguide modes to verify their applicability to mathematically complex EM problems. In particular, we propose to calculate the fundamental and the some higher order modes for both transverse electric and transverse magnetic cases in circular waveguides. This is mathematically challenging due to the nature of geometry and the associated boundary conditions of circular structures. The results confirm the possibility of applying VQE for mathematically complex EM problems, announcing its potential to scale up and solve high-dimensional, large-scale EM problems where classical algorithms can fail.

This study investigates the performance of a reverberation chamber under random boundary conditions with a uniform field distribution to take into account the irregularities of the wall, in particular at high frequencies such as the millimeter band. Wall irregularities cause a more random field inside the chamber than a perfect electric conductor. An FDTD code is optimized to simulate random boundaries based reverberation chamber with a stirrer. The number of uncorrelated positions are evaluated by using the mode stirrer to characterize random boundaries, thus bringing a more chaotic behavior inside the chamber. The Kolmogorov-Smirnov statistical analysis further exhibits that the chamber’s field distribution aligns well with theoretical expectations. Results point out the suitability of a reverberation chamber for electromagnetic compatibility tests and wireless device characterization in the millimeter band, where the walls are not ideal. These findings reinforce the effectiveness of reverberation chambers in controlled electromagnetic environments.

Reverberation chambers serve a critical function in assessing the performance of wireless communication systems by providing controlled testing environments. A key aspect of this evaluation is the manipulation of the Rician K-factor within these chambers. The primary challenge arises from the inherently low values of the Rician K-factor, which require deliberate adjustments to accurately simulate real-world communication conditions. Traditionally, the tuning of the Rician K-factor involves the introduction of lossy elements, which decreases the quality factor of the chamber and necessitates additional costs for amplification to sustain the desired electromagnetic field intensity. An alternative approach focuses on the selection of electromagnetic states within the chamber, which does not significantly alter the field amplitude but may affect its statistical properties. The proposed method represents a trade-off between these two strategies, enabling a reduction in amplification costs while preserving the statistical fidelity of the electromagnetic field distribution to remain Rician. This method has been successfully implemented across different chamber configurations and has been explored in various frequency ranges.

The analysis of stochastic electromagnetic fields is gaining more and more relevance due to the exponential growth of complex high-performance electronic systems. Stochastic electromagnetic fields are characterized by auto and cross-correlation functions which can be obtained from experimental data. Different methods have been proposed for the numerical propagation of correlation information within the near-field region of a stochastic radiator. As a guideline for general geometries, near-field Green's functions combined with the method of moments can be used for the numerical estimation of field correlations in the near-field surrounding a device under test. In the ray-tracing limit, a more insightful propagation method based on the Wigner transformation has been devised, through which it is also possible to estimate the propagation of stochastic fields in the near-field. In this paper we report on the implementation of the proposed guide in the open source Python programming language, accessible through the IEEE Standard Association repository to ensure the dissemination of the standard and encourage the development of new versions.

Wireless communication technology has become important in modern life. Real-world radio environments present significant challenges, particularly concerning latency and multipath fading. A promising solution is represented by reconfigurable intelligent surfaces (RIS), which can manipulate electromagnetic waves to enhance transmission quality. In this study, we introduce a novel approach that employs the quantum approximate optimization algorithm (QAOA) to efficiently configure RIS in multipath environments. Applying the spin glass (SG) theoretical framework to describe chaotic systems, along with a variable noise model, we propose a quantum-based minimization algorithm to optimize RIS in various electromagnetic scenarios affected by multipath fading. The method involves training a parameterized quantum circuit using a mathematical model that scales with the size of the RIS. When applied to different EM scenarios, it directly identifies the optimal RIS configuration. This approach eliminates the need for large datasets for training, validation, and testing, streamlines, and accelerates the training process. Furthermore, the algorithm will not need to be rerun for each individual scenario. In particular, our analysis considers a system with one transmitting antenna, multiple receiving antennas, and varying noise levels. The results show that QAOA enhances the performance of RIS in both noise-free and noisy environments, highlighting the potential of quantum computing to address the complexities of RIS optimization and improve the performance of the wireless network.

Reverberation chambers (RCs) have been widely employed in bioelectromagnetic large-scale rodent bioassays, to investigate potential effects of lifetime RF exposures. Numerical RF dosimetry has been of the utmost importance in determining the range of exposures, in terms of whole-body and organ specific absorption rate (SAR), and its uniformity across large animal cohorts, and therefore may influence key decisions in the design of in-vivo animal studies (exposure levels, number of animals per room, etc.). In this work, we expand prior research on rodents’ numerical dosimetry in RCs based on a realistic RC configuration, comprising a mode stirrer and various types of antennas. The exposure prediction modeling involves a MonteCarlo (MC) approach recently developed by the authors, taking into account the animals size distribution, as well as their posture, position and orientation within individual cages. The feasibility of conducting whole-RC dosimetry within a multivariate MC framework for large rodent cohorts at 900 MHz was demonstrated, showing larger whole-body SAR (wbSAR) variability than previously estimated, and suggesting that exposures in some of the cages may be consistently higher or lower than a cohort mean wbSAR target.

Current NIQST machines have a relatively small number of qubits and a limited circuit depth. In this regard, a class of quantum variational algorithms (VQAs) have been developed to be able to split the computational cost between classical and quantum hardware. In this study, the VQE algorithm has been used in order to solve the problem of propagation modes in rectangular waveguides described analytically by the Helmholtz partial differential equation. The IBM quantum device called manila was used in these calculations to provide noise present in physical hardware. In order to implement the VQE, IBM's proprietary framework called Qiskit has been used. The results show good agreement between calculations run on simulated quantum hardware and those run on real quantum hardware. This agreement between theory and experiment is evidence that high fidelity modeling of electromagnetic fields utilizing quantum hardware is possible.

In this paper we evaluated the performance of a reconfigurable intelligent surface tested with a fifth generation signal provided by a commercial fifth generation base station. We adopted a reverberation chamber as a real life propagating environment. Tests were conducted at the millimeter wave frequency range. This measurement campaign was carried out under the H2020 European project RISE-6G and a collaboration program between TIM S.p.A., Nokia and Università Politecnica delle Marche.

Current quantum computers (QCs) belong to the noisy intermediate-scale quantum (NISQ) class, characterized by noisy qubits, limited qubit capabilities, and limited circuit depth. These limitations have led to the development of hybrid quantum-classical algorithms that split the computational cost between classical and quantum hardware. Among the hybrid algorithms, the variational quantum eigensolver (VQE) is mentioned. The VQE is a variational quantum algorithm designed to estimate the eigenvalues and eigenvectors of a system on universal-gate quantum architectures. A canonical problem in electromagnetics is the computation of eigenmodes within waveguides. Following the finite difference method, the wave equation can be recast as an eigenvalue problem. This work exploits the quantum superposition and entanglement in quantum computing to solve the square waveguide mode problem. This algorithm is expected to demonstrate exponentially efficiency over classical computational techniques as the qubit count increases. The simulations were performed on IBM’s three-qubit quantum simulator, Qasm IBM Simulator. A shot-based simulation was performed considering computationally based measurements of the quantum hardware. The results of the probabilistic read-out, reported in terms of 2-D eigenmode field distributions, are close to ideal values with a few number of qubits, confirming the possibility to exploit the quantum advantage to formulate innovative eigensolvers.

Reverberation chambers play a fundamental role in evaluating the efficacy of wireless communication systems by providing controlled environments for testing. One crucial aspect of this assessment is the manipulation of the Rician K factor within these chambers. The inherent challenge lies in the typically low values of the Rician K factor, necessitating intentional adjustments to replicate real-world communication scenarios accurately. This paper aims to expound upon the statistical analysis of the Rician K factor, focusing on the various measures undertaken to modulate this parameter. The experimentation involves a comprehensive examination of actions taken to tune the Rician K factor. One of the key strategies involves the strategic insertion of lossy elements within the reverberation chamber. These elements introduce intentional signal attenuation, impacting the Rician K factor and contributing to a more realistic simulation of wireless communication conditions. The paper delves into the intricacies of selecting and placing these lossy elements to achieve the desired level of signal degradation and, consequently, an appropriate Rician K factor. Furthermore, the investigation considers the positioning of these lossy elements within the reverberation chamber. The spatial distribution and arrangement of these elements can significantly influence the electromagnetic field characteristics, affecting the Rician K factor. In addition to the insertion and placement of lossy elements, the orientation of the receiver within the chamber emerges as another factor under analysis. The paper explores how variations in receiver orientation impact the statistics of the Rician K factor