arXiv subjects

George C. Alexandropoulos

Publications and source records attributed to George C. Alexandropoulos.

At least 19 recordsLinked to original sources

arXiv2026

Cross-View Vision-Aided Proactive BS Selection and Beam Prediction for mmWave V2I Communications

This paper investigates environmental-sensing-aided proactive base station (BS) selection and beam prediction for millimeter-wave (mmWave) vehicle-to-infrastructure (V2I) wireless systems. We exploit onboard panoramic street-view images and a preloaded satellite map to predict communication-relevant environmental information around the vehicle, including nearby building footprints and heights. The predicted height map provides a compact environmental prior and is combined with historical mobility information to jointly predict the next-slot line-of-sight (LoS) state, transmission rate, and transmit and receive beam selections. On our dataset covering different real-world regions across New South Wales, Australia, the proposed framework achieves 91.4% LoS classification accuracy, 0.638 bps/Hz mean absolute error of data rate prediction, and more than 40% higher transmission rate than the conventional reactive baseline in geographically unseen regions, outperforming all evaluated deployable learning-based baselines. The dataset and code will be released at https://github.com/Huzijiao/Cross-view_V2I

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arXiv2026

Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces

The recently envisioned goal-oriented communications paradigm requires machine learning inference to be performed directly on wirelessly transferred data. This paper presents an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) system that operates as an Extreme Learning Machine (ELM) to execute Over-The-Air (OTA) binary classification. To reduce hardware complexity, the receiver is equipped with cascaded metasurfaces terminating in a single radio-frequency chain. A front metasurface layer applies a fixed nonlinear response to the incoming signal, acting as the ELM's activation function. Subsequent tunable linear metasurface layers physically approximate the trained network weights directly in the wave domain. Numerical evaluations across diverse datasets showcase that our XL MIMO architecture achieves classification accuracy comparable to idealized digital models, thereby proving the viability of low-complexity, wave-domain OTA learning.

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arXiv2026

$Z^2$-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN

With the progression in open and disaggregated 6G radio access networks, it is expected that the system will be able to host multi-vendors. In order to host multi-vendors, it is essential that AI-assisted control loops remain safe, verifiable, and auditable under concurrent operator intents and untrusted model inputs. The existing studies address the agentic coordination, formal intent constraints, zero-trust prompt verification and cryptographic accountability in isolation, which leaves pre-realization safety, continuous semantic verification and cross-domain audit incomplete when used individually. In this regard, we propose zero-knowledge auditable control and zero-trust verifiable agentic intent architecture ($Z^2$-ACT), which integrates the aforementioned four primitives across the non-real-time and near-real-time RICs. We encode the typed Intent Contracts as operator goals while the large language model inputs are only admitted after a practical adversarial intent check. The skill sequences in the proposed study are released only when a self-management gate is satisfied while every successful commit is recorded as a binding commitment with a zero-knowledge proof. Our experimental evaluation on public ColO-RAN measurements compares the full architecture against targeted ablations and a conventional reinforcement-learning baseline. A live large language model is used in the non-real-time path to translate operator intents into Intent Contracts; we report translation accuracy, the rate of invalid or hallucinated contracts, non-real-time latency, and behavior under adversarial or misleading intents. Near-real-time control remains trace-driven on the public KPM sequences. Results indicate improved actuation filtering and attack resilience at modest latency and signaling cost inside the near-real-time envelope.

cs.CR↗

arXiv2026

Energy Efficient Multi-User Beamforming and 3D Position Optimization for SIM-Assisted UAVs

This paper studies energy-efficient downlink multi-user transmissions with unmanned aerial vehicle (UAV) communication systems equipped with stacked intelligent metasurfaces (SIM), enabling wave-domain analog beamforming through multiple cascaded metasurface layers, while low-dimensional digital precoding is carried out using a limited number of transmit radio-frequency chains. This architecture enables flexible electromagnetic wave manipulation with reduced hardware complexity, making it particularly suitable for energy-constrained aerial platforms. We formulate a hardware-aware energy-efficiency (EE) maximization problem aiming to jointly optimize the digital precoder, the phase shifts of all SIM layers, and the three-dimensional UAV position under transmit-power, SIM operation, and UAV deployment constraints. The resulting problem is highly non-convex due to the fractional objective, the cascaded SIM structure and the unit-modulus phase constraints of the constituent metasurface layers, as well as the non-linear UAV-dependent channel. To address these challenges, we develop a transform-based alternating optimization framework that combines Dinkelbach's method, dual and quadratic transforms, to enable closed-form digital beamforming, Riemannian manifold optimization for SIM phase shifts, and successive convex approximation (SCA) for UAV positioning. Convergence and complexity analyses are provided to characterize the proposed algorithm. The presented numerical results showcase that the proposed joint design significantly improves EE compared with fully digital and maximum ratio transmission benchmark schemes, while revealing important design trade-offs among transmit power, SIM size, and the number of its constituent stacked layers.

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arXiv2026

Secret Key Rate Analysis of RIS-Assisted THz MIMO CV-QKD Systems under Access-Constrained Eavesdropping

A reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) continuous-variable quantum key distribution (CV-QKD) system operating at terahertz (THz) frequencies, in which a transmitter, Alice, encodes secret keys using Gaussian-modulated coherent states and communicates them to a legitimate receiver, Bob, is considered in this paper. The composite wireless channel, comprising the direct Alice-Bob link and the RIS-assisted reflected link, is modeled as a passive linear Gaussian quantum channel, enabling a unitary dilation that preserves the canonical commutation relations. The security of the considered system is investigated under collective Gaussian entangling attacks by introducing a practical access-constrained eavesdropping model, in which an eavesdropper, Eve, is assumed to access only the environmental modes associated with physically accessible propagation segments. A unified analytical framework is developed to derive the achievable secret key rate (SKR) across all single-segment, pairwise-segment, and full-segment access scenarios, assuming homodyne detection and reverse reconciliation at Bob. Furthermore, an optimization framework is developed to determine the optimal RIS phase configuration matrix and transmitter/receiver beam-splitter parameters that maximize the SKR performance. The resulting optimization problem is efficiently solved using particle swarm optimization. Numerical results are presented to demonstrate the system's performance with respect to various free parameters. It is showcased that the secrecy performance strongly depends on Eve's accessible propagation segments, with the Alice-RIS channel constituting the most security-critical segment.

cs.IT↗

arXiv2026

Electromagnetics-Compliant Optimization of Dynamic Metasurface Antennas for Bistatic Sensing

Dynamic Metasurface Antennas (DMAs) are recently attracting considerable research interests due to their potential to enable low-cost, reconfigurable, and highly scalable antenna array architectures for next generation wireless systems. However, most of the existing literature relies on idealized models for the DMA operation, often overlooking critical structural and physical constraints inherent to their constituent metamaterials. In this paper, leveraging a recently proposed model for this antenna architecture incorporating physically consistent modeling of mutual coupling and waveguide propagation losses, we optimize DMA-based transmission for bistatic sensing. A tractable approximation for the DMA response is first presented, which enables efficient optimization of the dynamically reconfigurable Lorentzian-constrained responses of the array's metamaterials. In particular, we formulate a robust beamforming optimization problem with the objective to minimize the worst-case position error bound, in the presence of spatial uncertainties for the environment's scatterers as well as synchronization uncertainties at the analog combining multi-antenna receiver. To address the resulting high computational complexity due to the possibly excessive number of metamaterial-based antennas and their operation constraints, two low complexity beamforming design approaches are presented that perform offline searching over a novel beam codebook. The accuracy of all presented DMA designs is assessed by means of Monte Carlo simulations for various system parameters, confirming that accurately modeling mutual coupling is essential for maintaining increased localization performance. It is also shown that, even under positioning and synchronization uncertainties, the proposed designs yield accuracy comparable to their fully digital and analog counterparts, while adhering to the structural DMA constraints.

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arXiv2026

TenSIM: Tensor-Based Channel Estimation for MIMO Systems with Stacked Intelligent Metasurfaces

Stacked intelligent metasurfaces (SIMs) are emerging as a promising architecture for sixth-generation (6G) and beyond wireless systems, enabling richer electromagnetic-wave manipulation than conventional single-layer metasurfaces. However, strong inter-layer coupling and multilinear parameter interactions make accurate, scalable channel estimation challenging. This paper proposes TenSIM, a tensor-based channel-estimation framework for SIM-assisted multiple-input multiple-output (MIMO) systems. By exploiting a structured SIM training protocol, TenSIM derives two parity-dependent observation models: a PARAllel FACtor (PARAFAC) model for odd-layer SIMs and a Tucker model for even-layer SIMs. These formulations decouple the transmitter-SIM and SIM-receiver channel factors while accounting for inter-layer wave coupling. Based on these tensor models, we develop alternating least squares estimators, establish rank-based identifiability conditions using the associated design matrices, and provide practical sufficient conditions for full-column-rank training designs, including scaling ambiguities. Numerical results reveal the main trade-offs. Both TenSIM-PARAFAC and TenSIM-Tucker improve with signal-to-noise ratio and training diversity, outperforming unstructured least-squares baselines by exploiting the tensor structure of the SIM cascade. TenSIM-PARAFAC offers better scalability, lower complexity, and stronger robustness to inter-layer spacing, whereas TenSIM-Tucker can achieve more accurate channel reconstruction when sufficient training and strong layer coupling are available. The framework also remains effective under imperfect or blind SIM training with additional pilot diversity. Overall, TenSIM offers a unified, physically interpretable approach to channel estimation in SIM-assisted MIMO systems, with explicit identifiability, complexity, and performance trade-offs.

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arXiv2026

RIS Beamforming under Element-Level Variations: Statistical Characterization and Robust Design

In this paper, a novel analytical framework to characterize the impact of element-level variations on the radiation characteristics of reconfigurable intelligent surfaces (RISs) is introduced. Specifically, a statistical model is proposed to capture the effects of varactor capacitance fluctuations on the RIS reflection coefficients, and, subsequently, on the resulting power radiation pattern; both low- and large-variance independent perturbation scenarios, are investigated. Leveraging the proposed statistical model, a low complexity greedy optimization methodology is presented, having the goal to optimize the expected RIS radiation power, thereby, generating inherently robust configurations. Furthermore, the analytical proposed model serves as an efficient alternative to computationally expensive Monte Carlo simulations, enabling the quantification of element sensitivity to manufacturing and operational tolerances. As demonstrated, optimizing the mean power pattern significantly enhances system performance under element-level variations. For typical RIS sizes (e.g., 32x32 or 64x64), a main lobe gain exceeding 2 dB and a sidelobe suppression of approximately 10 dB are achieved.

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arXiv2026

Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems

In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete information, since active sensing and frequent communications increase the risk of exposure. As a result, AUVs primarily rely on passive observation, an approach that yields incomplete local perception and limited task efficiency. Although underwater acoustic communications can mitigate this limitation through information sharing, they are simultaneously constrained by long delays, severe interference, low reliability, and the risk of covert exposure. Existing communications-oriented multi-agent reinforcement learning (MARL) studies often model communication as an ideal information flow, whereas traditional communication optimization primarily focuses on link-level performance. However, both are insufficient to characterize the actual contribution of perceptual information to cooperative tasks under realistic conditions of covert physical communications. This paper proposes a Sensed Information Value Realization Multi-Agent Reinforcement Learning (SVR-MARL) framework that leverages practical information to characterize the utility of information for cooperative tasks and learns distributed cooperative policies under realistic communication and covert constraints. Through a case study of covert multi-AUV cooperative localization and tracking, the potential of the proposed framework to improve collaborative task efficiency while reducing unnecessary communication and exposure risks is demonstrated.

cs.LG↗

arXiv2026

Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach

This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS). Since localization pilot transmissions dominate the energy budget of power-constrained devices, we introduce a low-overhead feedback link from the Base Station (BS) to the user to enable dynamic uplink power control. To navigate the discrete and decentralized nature of this active sensing problem, we propose a novel Dual-Agent (DA) deep learning framework that jointly optimizes the discrete RIS phase profiles and the UE's transmit power in real time. Specifically, our approach employs a hybrid training methodology integrating the neuroevolution paradigm with supervised learning, effectively overcoming the non-differentiability of discrete phase responses from the RIS unit elements and the strict information bottleneck of single-bit feedback messages for pilot power control. The proposed DA active sensing framework can be applied with both single- and multi-antenna BSs, the latter with only minor modifications in the structure of one NN: an additional output branch with appropriate structure is included for the latter case to select a valid digital combiner from a finite set. Extensive numerical simulations demonstrate that the proposed scheme achieves highly accurate and robust tracking across diverse target motion models, outperforming extended Kalman and particle filters, as well as, machine learning-based trackers. Furthermore, in static localization, it is shown to significantly outperform traditional fingerprinting schemes, deep reinforcement learning baselines, and standard backpropagation-based estimators.

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arXiv2026

Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter

Online latent state estimation constitutes a fundamental challenge within the artificial intelligence field, serving as a foundational tool for diverse applications, including sequential decision making, anomaly and change-point detection. In this paper, a novel online distributed sensing framework, where agents collaborate and exchange information to perform latent state estimation, is presented. The proposed estimator combines available partial domain knowledge with the representation capabilities of deep neural networks. In particular, the designed sensing framework incorporates prior estimates, optimized consensus weights, and Kalman-like recursive updates to perform decentralized inference, without relying on knowledge of noise statistics. Extensive experiments on linear, chaotic (Lorenz), and practical wireless tracking environments reveal that the proposed Covariance-Agnostic Neural Kalman Consensus Filter (CA-NKCF) outperforms traditional distributed Kalman and particle filters as well as purely model-free deep neural networks, exhibiting robustness even when the underlying motion and observation models are misspecified. It is also demonstrated that CA-NKCF's performance advantage remains stable across varying noise levels, random communication topologies, latent state dimensions, and observation clutter densities induced by scattering objects in wireless systems.

cs.LG↗

arXiv2026

A Physics-Informed Hierarchical Neural Network for Microwave Scattering Analysis of 3D PEC Targets

Accurate modeling of scattering from three-dimensional (3D) perfectly electrically conducting (PEC) targets at microwave frequencies constitutes a fundamental objective in computational electromagnetics, particularly for radar cross section (RCS) prediction and microwave scattering analysis. Classical solvers, such as the method of moments and the Multilevel Fast Multipole Algorithm (MLFMA), although provide high physical fidelity, they become costly under scenarios of repeated queries involving many incidence configurations or frequencies, whereas purely data-driven surrogates often lack accuracy on geometrically complex targets. This paper proposes a U-shaped physics-informed artificial neural network (U-PINet) for 3D microwave scattering analysis. Inspired by the near-far field decomposition of MLFMA, U-PINet combines a near-field graph encoder, parameterized by learnable univariate basis functions, with a hierarchical multi-scale fusion module organized on an octree partition. The proposed network is trained against a discretized residual of the electric-field integral equation at surface collocation points, without requiring reference current labels. Experiments on canonical and geometrically complex 3D PEC targets, conducted under multiple frequency and polarization configurations and assessed through bistatic RCS reconstruction, showcase that U-PINet outperforms representative physics-informed baselines, and yields substantial runtime savings over the classical MLFMA solver under repeated-query scenarios.

cs.LG↗

arXiv2026

Uplink Channel Estimation for Multi-User MISO Systems Assisted by a Fluid Reconfigurable Intelligent Surface

Fluid reconfigurable intelligent surfaces (FRISs) have recently emerged as a promising paradigm for wireless communications, wherein the reflecting elements can dynamically select their effective radiating positions from a dense preset grid, thereby introducing an additional degree of freedom. In contrast to conventional RIS architectures, FRISs can achieve spatial diversity with fewer physical elements. However, beyond the cascaded channel structure, FRIS-assisted systems are also affected by uncertainties arising from element-position mismatches caused by calibration inaccuracies or motion errors, which may degrade channel state information. To the best of our knowledge, channel estimation (CE) for FRIS-assisted systems under position uncertainty remains unexplored. To fill this gap, we propose a CE framework for a multi-user FRIS-assisted uplink system based on a two-time-scale FRIS configuration protocol that captures both reflection phase-shift and element-motion dynamics. By capitalizing on orthogonal pilot sequences and tensor modeling, we derive a closed-form solution that jointly estimates the individual channels and the motion-induced phase coefficients. Numerical results demonstrate notable performance in the presence of unknown position deviations.

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arXiv2026

Near-Field Beam Focusing Characterization for 2D Waveguide-Fed Metasurface Antennas

Two-dimensional (2D) waveguide-fed metasurfaces enable scalable antenna apertures through guided wave excitation of distributed radiating elements. However, the resulting non-uniform excitation challenges classical interpretations of near-field characteristics. Using a physics-compliant model, this paper analyzes the near-field beam focusing behavior of such architectures. We derive asymptotic scaling laws for the beamforming gain, showcasing that the power-normalized gain scales linearly with the number of radiating elements. Furthermore, we introduce a normalized beam-depth formulation and obtain a compact analytic expression that characterizes the transition to far-field-like behavior. The presented analysis is validated against simulations based on the full electromagnetic model, confirming the accuracy of the derived scaling laws and beam-depth limits.

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arXiv2026

A Near-Field Compatible Model for 2D Waveguide-Fed Metasurfaces

This paper presents a novel physically consistent analytical model for two-dimensional (2D) waveguide-fed metasurface antennas that is based on the discrete dipole approximation. The proposed framework extends previous works deriving power-consistent constraints on the magnetic polarizability tensor, leading to closed-form expressions for the effective polarizabilities. The model is validated through full-wave simulations for multi-feed settings, and is extended to a near-field compatible~formulation enabling accurate predictions in the radiating near-field.

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arXiv2026

Robust Beamforming for Cell-Free Systems with Parallel-Plate-Waveguided Dynamic Metasurfaces

Dynamic Metasurface Antennas (DMAs) constitute a promising solution for extremely large antenna arrays, requiring lower power consumption and reduced hardware cost as compared to conventional phased arrays. In this paper, we consider a cell-free Orthogonal Frequency Division Multiplexing (OFDM) system comprising multiple Base Stations (BSs) equipped with parallel-plate-waveguided DMAs, which aims to serve multiple users in the downlink direction. Focusing on a realistic frequency-selective model for the response-tunable elements of each DMA panel, and targeting to surpass the necessity of centralized designs that rely on a central processing unit with high computational power, we present a distributed optimization framework with minimal control information exchange for the frequency-selective analog and digital beamforming matrices of the multiple BSs, having the system spectral efficiency maximization as the design objective. Considering imperfect Channel State Information (CSI) availability at each BS, we devise a parallel decomposition framework for the configuration of the tunable parameters of each DMA-based BS. Our numerical results showcase the robustness of the proposed distributed beamforming design over different CSI conditions, and quantify the critical role of taking into account mutual coupling during the DMA design process.

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arXiv2026

2D Waveguide-Fed Metasurfaces: Physically Consistent Modeling, Validation, and Optimization

Antenna array architectures based on programmable metasurfaces are emerging as a promising solution for scalable implementations of the eXtremely Large Multiple-Input Multiple-Output (XL-MIMO) systems paradigm, envisioned for 6-th Generation (6G), and beyond, wireless networks. However, their accurate modeling, quantifying the role of key structural features, such as strong mutual coupling and guided-wave excitation, remains challenging, amplifying the need for physically consistent representations of the constituent metamaterial elements. In this paper, capitalizing on the coupled dipole formulation, we develop a comprehensive electromagnetics-compliant framework for 2-Dimensional (2D) waveguide-fed metasurface antennas. The proposed model extends relevant existing modeling approaches by incorporating both electric and magnetic dipoles' responses, accounting for multiple excitation feeds, and enabling accurate characterization in both the near- and far-field regimes. Radiation-reaction corrections based on passivity constraints are derived and shown to ensure the passivity of the overall dipole system. In addition, we present a novel input impedance model for the considered architecture enabling explicit computation of the accepted power, and facilitating efficient beamforming design under realistic power constraints. All modeling components developed in this paper are validated against full-wave electromagnetic simulations. Furthermore, the analytical structure of the proposed model enables the formulation of a differentiable beamforming design optimization problem over both the considered metasurface geometry and its feed excitations. The presented numerical results demonstrate the effectiveness of the proposed modeling framework in achieving both directive beamforming and sector-wide coverage.

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arXiv2026

Low-Complexity Learning-Based Beamforming for Ultra-Massive MIMO THz Communications

Terahertz (THz) communications have emerged as a key technology for escalating data rates in future generation wireless networks. However, severe propagation losses at THz frequencies pose significant challenges, which can be mitigated via ultra-massive multiple-input multiple-output (UM-MIMO) systems employing highly directional transmissions. To this end, codebook-based analog beamforming constitutes a realistic solution, eliminating the need for explicit channel estimation. However, in UM-MIMO systems, the use of extremely narrow beams makes beam training and alignment increasingly challenging, leading to a substantial increase in the number of codewords to be tested and, thus, to high computational complexity. In this paper, a novel artificial neural network architecture for low-complexity beam training in UM-MIMO THz systems is presented, which does not require a constant feedback link between transmitter and receiver to obtain the best beamformer and combiner pair. An inception and residual network, which is trained based on the received signal powers using the transmit and receive codewords generated from predefined hierarchical codebooks, is designed. Our numerical investigations demonstrate that the proposed machine learning approach significantly reduces the complexity of UM-MIMO transmit and receive beamforming design, as compared to the standard exhaustive and hierarchical beam searching methods.

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