arXiv subjects

George C. Alexandropoulos

Publications and source records attributed to George C. Alexandropoulos.

At least 55 records · Page 3Linked to original sources

arXiv2026

Decentralized Cooperative Beamforming for BDRIS-Assisted Cell-Free MIMO OFDM Systems

In this paper, a wideband cell-free multi-stream multi-user Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system is considered operating within a smart wireless environment enabled by multiple Beyond Diagonal Reconfigurable Intelligent Surfaces (BDRISs). A novel decentralized active and passive beamforming framework, robust to imperfect channel state availability and with minimal cooperation among the system's multiple Base Stations (BSs) for deciding the final configurations of the shared BDRISs, is proposed, which aims to substantially reduce the overhead inherent in centralized solutions necessitating a central processing unit of high computational power. By considering a Dynamic Group-Connected (DGC) BDRIS architecture with frequency-selective responses per unit element, we formulate the system's sum-rate maximization problem with respect to the tunable capacitances and permutation matrices of the BDRISs as well as the precoding matrices of the BSs, which is solved via successive concave approximation and alternating projections as well as consensus-based updates for the BDRISs' design. Through extensive simulation results, it is showcased that the proposed robust decentralized cooperative approach with diverse BDRIS architectures outperforms non-cooperation benchmarks. It is also demonstrated that the considered DGC BDRIS architecture is able to provide sum-rate performance gains sufficiently close to the more complex fully-connected BDRIS structure.

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arXiv2026

Robust Consensus-Based Distributed Beamforming for Wideband Cell-free Multi-RIS MISO Systems

The cell-free networking paradigm constitutes a revolutionary architecture for future generations of wireless networks, which has been recently considered in synergy with Reconfigurable Intelligent Surfaces (RISs), a promising physical-layer technology for signal propagation programmability. In this paper, we focus on wideband cell-free multi-RIS-empowered Multiple-Input Single-Output (MISO) systems and present a decentralized cooperative active and passive beamforming scheme, aiming to provide an efficient alternative towards the cooperation overhead of available centralized schemes depending on central processing unit. Considering imperfect channel information availability and realistic frequency selectivity behavior of each RIS's element response, we devise a distributed optimization approach based on consensus updates for the RISs' phase configurations. Our simulation results showcase that the proposed distributed design is superior to centralized schemes that are based on various Lorentzian-type wideband modeling approaches for the RISs.

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arXiv2026

Metasurfaces-Enabled Wave Computing for Future Wireless Systems: Opportunities and Challenges

The next generations of wireless networks are envisioned to integrate communications, sensing, and computing into a unified platform, demanding ultra-high data rates, submillisecond latency, and unprecedented energy efficiency. However, conventional digital processors face limitations in scalability, cost, and power consumption that hinder this vision. Wave computing, enabled by programmable metasurfaces, offers an alternative paradigm according to which signal processing operations are implemented in the domain of the propagation of electromagnetic waves. This approach transforms metasurfaces from passive wavefront shapers into functional analog processors capable of executing tasks such as beamforming, sensing, imaging, and machine learning at the speed of light with minimal power consumption. This article provides an overview of metasurface-enabled wave computing, highlighting its fundamental principles and key application scenarios for future wireless systems, including integrated sensing and communications, artificial intelligence acceleration, over-the-air channel estimation, and computational electromagnetic imaging. Future research directions are outlined in response to the major open challenges of the technology, aiming to enable large-scale deployment of wave computing in practical wireless networks.

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arXiv2026

Learning the Basis: A Kolmogorov-Arnold Network Approach Embedding Green's Function Priors

The Method of Moments (MoM) is constrained by the usage of static, geometry-defined basis functions, such as the Rao-Wilton-Glisson (RWG) basis. This letter reframes electromagnetic modeling around a learnable basis representation rather than solving for the coefficients over a fixed basis. We first show that the RWG basis is essentially a static and piecewise-linear realization of the Kolmogorov-Arnold representation theorem. Inspired by this insight, we propose PhyKAN, a physics-informed Kolmogorov-Arnold Network (KAN) that generalizes RWG into a learnable and adaptive basis family. Derived from the EFIE, PhyKAN integrates a local KAN branch with a global branch embedded with Green's function priors to preserve physical consistency. It is demonstrated that, across canonical geometries, PhyKAN achieves sub-0.01 reconstruction errors as well as accurate, unsupervised radar cross section predictions, offering an interpretable, physics-consistent bridge between classical solvers and modern neural network models for electromagnetic modeling.

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arXiv2025

Over-the-Air Goal-Oriented Communications

Goal-oriented communications offer an attractive alternative to the Shannon-based communication paradigm, where the data is never reconstructed at the Receiver (RX) side. Rather, focusing on the case of edge inference, the Transmitter (TX) and the RX cooperate to exchange features of the input data that will be used to predict an unseen attribute of them, leveraging information from collected data sets. This chapter demonstrates that the wireless channel can be used to perform computations over the data, when equipped with programmable metasurfaces. The end-to-end system of the TX, RX, and MS-based channel is treated as a single deep neural network which is trained through backpropagation to perform inference on unseen data. Using Stacked Intelligent Metasurfaces (SIM), it is shown that this Metasurfaces-Integrated Neural Network (MINN) can achieve performance comparable to fully digital neural networks under various system parameters and data sets. By offloading computations onto the channel itself, important benefits may be achieved in terms of energy consumption, arising from reduced computations at the transceivers and smaller transmission power required for successful inference.

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arXiv2025

RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization

This chapter overviews the concept of Smart Wireless Environments (SWEs) motivated by the emerging technology of Reconfigurable Intelligent Surfaces (RISs). The operating principles and state-of-the-art hardware architectures of programmable metasurfaces are first introduced. Subsequently, key performance objectives and use cases of RIS-enabled SWEs, including spectral and energy efficiency, physical-layer security, integrated sensing and communications, as well as the emerging paradigm of over-the-air computing, are discussed. Focusing on the recent trend of Beyond-Diagonal (BD) RISs, two distributed designs of respective SWEs are presented. The first deals with a multi-user Multiple-Input Single-Output (MISO) system operating within the area of influence of a SWE comprising multiple BD-RISs. A hybrid distributed and fusion machine learning framework based on multi-branch attention-based convolutional Neural Networks (NNs), NN parameter sharing, and neuroevolutionary training is presented, which enables online mapping of channel realizations to the BD-RIS configurations as well as the multi-user transmit precoder. Performance evaluation results showcase that the distributedly optimized RIS-enabled SWE achieves near-optimal sum-rate performance with low online computational complexity. The second design focuses on the wideband interference MISO broadcast channel, where each base station exclusively controls one BD-RIS to serve its assigned group of users. A cooperative optimization framework that jointly designs the base station transmit precoders as well as the tunable capacitances and switch matrices of all metasurfaces is presented. Numerical results demonstrating the superior sum-rate performance of the designed RIS-enabled SWE for multi-cell MISO networks over benchmark schemes, considering non-cooperative configuration and conventional diagonal metasurfaces, are presented.

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arXiv2025

Blinding the Wiretapper: RIS-Enabled User Occultation in the ISAC Era

An undesirable consequence of the foreseeable proliferation of sophisticated integrated sensing and communications (ISAC) technologies is the enabling of spoofing, by malicious agents, of situational information (such as proximity, direction or location) of legitimate users of wireless systems. In order to mitigate this threat, we present a novel ISAC scheme that, aided by a reconfigurable intelligent surface (RIS), enables the occultation of the positions of user equipment (UE) from wiretappers, while maintaining both sensing and desired communication performance between the UEs and a legitimate base station (BS). To that end, we first formulate an RIS phase-shift optimization problem that jointly maximizes the sum-rate performance of the UEs (communication objective), while minimizing the projection of the wiretapper's effective channel onto the legitimate channel (hiding objective), thereby disrupting the attempts by a wiretapper of localizing the UEs. Then, in order to efficiently solve the resulting non-convex joint optimization problem, a novel manifold optimization algorithm is derived, whose effectiveness is validated by numerical results, which demonstrate that the proposed approach preserves legitimate ISAC performance while significantly degrading the wiretapper's sensing capability.

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arXiv2025

Over-the-Air Semantic Alignment with Stacked Intelligent Metasurfaces

Semantic communication systems aim to transmit task-relevant information between devices capable of artificial intelligence, but their performance can degrade when heterogeneous transmitter-receiver models produce misaligned latent representations. Existing semantic alignment methods typically rely on additional digital processing at the transmitter or receiver, increasing overall device complexity. In this work, we introduce the first over-the-air semantic alignment framework based on stacked intelligent metasurfaces (SIM), which enables latent-space alignment directly in the wave domain, reducing substantially the computational burden at the device level. We model SIMs as trainable linear operators capable of emulating both supervised linear aligners and zero-shot Parseval-frame-based equalizers. To realize these operators physically, we develop a gradient-based optimization procedure that tailors the metasurface transfer function to a desired semantic mapping. Experiments with heterogeneous vision transformer (ViT) encoders show that SIMs can accurately reproduce both supervised and zero-shot semantic equalizers, achieving up to 90% task accuracy in regimes with high signal-to-noise ratio (SNR), while maintaining strong robustness even at low SNR values.

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arXiv2025

RIS-Aided Localization and Sensing

High-precision localization and environmental sensing are essential for a new wave of applications, ranging from industrial automation and autonomous systems to augmented reality and remote healthcare. Conventional wireless methods, however, often face limitations in accuracy, reliability, and coverage, especially in complex non-line-of-sight (NLoS) environments. Reconfigurable Intelligent Surfaces (RISs) have emerged as a key enabling technology, offering dynamic control over the radio propagation environment to overcome these challenges. This chapter provides a comprehensive overview of RIS-aided localization and sensing, bridging fundamental theory with practical implementation. The core principles of the RIS technology are first described detailing how programmable metasurfaces can intelligently combat blockages, enhance signal diversity, and create virtual line-of-sight (LoS) links. The chapter then reviews a range of application scenarios where RISs can offer significant improvements. A significant portion of the chapter is dedicated to algorithmic methodologies, covering beam sweeping protocols, codebook-based techniques, and advanced optimization and machine learning strategies for both localization and sensing. To validate the theoretical concepts in real-world conditions, recent experimental results using an RIS prototype are detailed, showcasing the technology's efficacy and illustrating key performance trade-offs.

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arXiv2025

Sensing-Aided Near-Field Beam Tracking

The interplay between large antenna apertures and high carrier frequencies in future wireless systems gives rise to near-field communications, where the curvature of spherical wavefronts renders traditional far-field beamforming models inadequate. This chapter addresses the following fundamental questions on near-field operation: (i) What is the maximum distance where far-field approximations remain effective for path gain prediction and beam design? (ii) What level of position resolution is needed for accurate near-field beam focusing? (iii) How frequently must channel state information be updated to maintain highly directive bweamforming in dynamic scenarios? We develop an analytical framework for assessing near-field beamforming gain degradation due to mismatches between the focusing point and the coordinates of a user. Closed-form expressions for beam correlation, beam sensitivity to user movement, and the direction of fastest beamforming gain degradation are derived. A dynamic polar coordinate grid is also proposed for low complexity and adaptive near-field beam search. Furthermore, we introduce the novel concept of beam coherence time, quantifying the temporal robustness of focused beams and enabling proactive sensing-aided beam tracking strategies. The effect of microstrip losses on the preceding derivations is also analyzed. Finally, extensive simulation results validate the presented theoretical analysis and beam tracking method over randomly generated user trajectories.

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arXiv2025

Near-Field Multipath MIMO Channels: Modeling Reflectors and Exploiting NLOS Paths

Near-field (NF) communications is receiving renewed interest in the context of multiple-input multiple-output (MIMO) systems involving large physical apertures with respect to the signal wavelength. While line-of-sight (LOS) links are typically expected to dominate in NF scenarios, the impact of non-LOS (NLOS) components at both in centimeter- and millimeter-wave frequencies may be in general non-negligible. Moreover, although weaker than the LOS path, NLOS links may be essential for achieving multiplexing gains in MIMO systems. The commonly used NF channel models for NLOS links in the literature are based on the point scattering assumption, which is not valid for large reflectors such as walls, ceilings, and the ground. In this paper, we develop a generalized statistical NF MIMO channel model that extends the widely adopted point scattering framework to account for imperfect reflections from large surfaces. This model is then leveraged to investigate how the physical characteristics of these reflectors influence the resulting NF MIMO channel. In addition, using the proposed channel model, we analytically demonstrate for a multi-user scenario that, even when users are located within the NF regime, relying solely on LOS NF links may be insufficient to achieve multiplexing gains, thus exploiting NLOS links becomes essential. Our simulation results validate the accuracy of the proposed model and show that, in many practical settings, the contribution of NLOS components is non-negligible and must be carefully accounted for in the system design.

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arXiv2025

2D Waveguide-Fed Metasurface Antenna Arrays: Modeling and Optimization for Bistatic Sensing

This paper presents a physics-consistent framework for bistatic sensing incorporating a 2-Dimensional (2D) waveguide-fed metasurface antenna array capable of realizing eXtremely-Large Multiple-Input Multiple-Output (XL MIMO) apertures. A coupled-dipole model is presented that captures the array's mutual coupling due to both waveguide and free-space interactions, and a novel passivity constraint on the corresponding magnetic polarizabilities is proposed. Focusing on a bistatic sensing setup, we leverage a Neumann-series approximation of the array response model and derive the Cramer-Rao bound for multi-target parameter estimation, which is then incorporated into a sensing optimization formulation with respect to the metasurface's per-element resonance strength configuration. Simulation results on the position error bound in the radiative near field with the proposed design quantify the critical role of metamaterial placement in strongly coupled metasurface-based XL MIMO bistatic sensing systems.

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arXiv2025

MIMO Communications with 1-bit RIS: Asymptotic Analysis and Over-the-Air Channel Diagonalization

This paper presents an asymptotic analysis of Multiple-Input Multiple-Output (MIMO) systems assisted by a 1-bit Reconfigurable Intelligent Surface (RIS) under Ricean fading conditions. Using random matrix theory, we show that, in the asymptotic regime, the dominant singular values and vectors of the transmitter-RIS and RIS-receiver channels converge to their deterministic Line-of-Sight (LoS) components, almost irrespective of the Ricean factors. This enables RIS phase configuration using only LoS information through a closed-form Sign Alignment (SA) rule that maximizes the channel gain. Furthermore, when the RIS is asymptotically larger than the transceiver arrays, proper RIS configuration can render the end-to-end MIMO channel in the capacity formula asymptotically diagonal, thereby eliminating inter-stream interference and enabling Over-The-Air (OTA) spatial multiplexing without channel knowledge at the transmitter. Building on this result, a waterfilling-inspired SA algorithm that allocates RIS elements to spatial streams, based on the asymptotic singular values and statistical channel parameters, is proposed. Simulation results validate the theoretical analyses, demonstrating that the proposed schemes achieve performance comparable to conventional Riemannian manifold optimization, but with orders of magnitude lower runtime.

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arXiv2025

Filtering Jump Markov Systems with Partially Known Dynamics: A Model-Based Deep Learning Approach

This paper presents the Jump Markov Filtering Network (JMFNet), a novel model-based deep learning framework for real-time state-state estimation in jump Markov systems with unknown noise statistics and mode transition dynamics. A hybrid architecture comprising two Recurrent Neural Networks (RNNs) is proposed: one for mode prediction and another for filtering that is based on a mode-augmented version of the recently presented KalmanNet architecture. The proposed RNNs are trained jointly using an alternating least squares strategy that enables mutual adaptation without supervision of the latent modes. Extensive numerical experiments on linear and nonlinear systems, including target tracking, pendulum angle tracking, Lorenz attractor dynamics, and a real-life dataset demonstrate that the proposed JMFNet framework outperforms classical model-based filters (e.g., interacting multiple models and particle filters) as well as model-free deep learning baselines, particularly in non-stationary and high-noise regimes. It is also showcased that JMFNet achieves a small yet meaningful improvement over the KalmanNet framework, which becomes much more pronounced in complicated systems or long trajectories. Finally, the method's performance is empirically validated to be consistent and reliable, exhibiting low sensitivity to initial conditions, hyperparameter selection, as well as to incorrect model knowledge

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arXiv2025

Performance Analysis of One- and Two-way DV-QKD with MIMO FSO Communication Systems

This paper considers a multiple-input multiple-output (MIMO) wireless system wherein two legitimate users attempt to exchange secret keys over free-space optical (FSO) channels. Novel frameworks for the use of the one- and two-way discrete-variable quantum key distribution (DV-QKD) protocols, employing weak coherent pulses and decoy states, are presented. Focusing on the case where a photon-number-splitting attack is adopted by the eavesdropper and the legitimate multi-antenna receiver using threshold detection for the key extraction, novel expressions for the secret key rate and quantum bit error rate for both one- and two-way protocols are derived. The performance gain with larger MIMO configurations and the tradeoff between the performances with the one- and the two-way protocols with respect to the transmission distance of the legitimate FSO link are numerically assessed.

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arXiv2025

3D Gaussian Radiation Field Modeling for Integrated RIS-FAS Systems: Analysis and Optimization

The integration of reconfigurable intelligent surfaces (RIS) and fluid antenna systems (FAS) has attracted considerable attention due to its tremendous potential in enhancing wireless communication performance. However, under fast-fading channel conditions, rapidly and effectively performing joint optimization of the antenna positions in an FAS system and the RIS phase configuration remains a critical challenge. Traditional optimization methods typically rely on complex iterative computations, thus making it challenging to obtain optimal solutions in real time within dynamic channel environments. To address this issue, this paper introduces a field information-driven optimization method based on three-dimensional Gaussian radiation-field modeling for real-time optimization of integrated FAS-RIS systems. In the proposed approach, obstacles are treated as virtual transmitters and, by separately learning the amplitude and phase variations, the model can quickly generate high-precision channel information based on the transmitter's position. This design eliminates the need for extensive pilot overhead and cumbersome computations. On this framework, an alternating optimization scheme is presented to jointly optimize the FAS position and the RIS phase configuration. Simulation results demonstrate that the proposed method significantly outperforms existing approaches in terms of spectrum prediction accuracy, convergence speed, and minimum achievable rate, validating its effectiveness and practicality in fast-fading scenarios.

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arXiv2025

Joint Optimization of Cooperation Efficiency and Communication Covertness for Target Detection with AUVs

This paper investigates underwater cooperative target detection using autonomous underwater vehicles (AUVs), with a focus on the critical trade-off between cooperation efficiency and communication covertness. To tackle this challenge, we first formulate a joint trajectory and power control optimization problem, and then present an innovative hierarchical action management framework to solve it. According to the hierarchical formulation, at the macro level, the master AUV models the agent selection process as a Markov decision process and deploys the proximal policy optimization algorithm for strategic task allocation. At the micro level, each selected agent's decentralized decision-making is modeled as a partially observable Markov decision process, and a multi-agent proximal policy optimization algorithm is used to dynamically adjust its trajectory and transmission power based on its local observations. Under the centralized training and decentralized execution paradigm, our target detection framework enables adaptive covert cooperation while satisfying both energy and mobility constraints. By comprehensively modeling the considered system, the involved signals and tasks, as well as energy consumption, theoretical insights and practical solutions for the efficient and secure operation of multiple AUVs are provided, offering significant implications for the execution of underwater covert communication tasks.

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arXiv2025

AI-Aided Annealed Langevin Dynamics for Rapid Optimization of Programmable Channels

Emerging technologies such as Reconfigurable Intelligent Surfaces (RIS) make it possible to optimize some parameters of wireless channels. Conventional approaches require relating the channel and its programmable parameters via a simple model that supports rapid optimization, e.g., re-tuning the parameters each time the users move. However, in practice such models are often crude approximations of the channel, and a more faithful description can be obtained via complex simulators, or only by measurements. In this work, we introduce a novel approach for rapid optimization of programmable channels based on AI-aided Annealed Langevin Dynamics (ALD), which bypasses the need for explicit channel modeling. By framing the ALD algorithm using the MAP estimate, we design a deep unfolded ALD algorithm that leverages a Deep Neural Network (DNN) to estimate score gradients for optimizing channel parameters. We introduce a training method that overcomes the need for channel modeling using zero-order gradients, combined with active learning to enhance generalization, enabling optimization in complex and dynamically changing environments. We evaluate the proposed method in RIS-aided scenarios subject to rich-scattering effects. Our results demonstrate that our AI-aided ALD method enables rapid and reliable channel parameter tuning with limited latency.

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