arXiv subjects

George C. Alexandropoulos

Publications and source records attributed to George C. Alexandropoulos.

At least 91 records · Page 5Linked to original sources

arXiv2025

Doubly-Dispersive MIMO Channels with Stacked Intelligent Metasurfaces: Modeling, Parametrization, and Receiver Design

Introduced with the advent of statistical wireless channel models for high mobility communications and having a profound role in communication-centric (CC) integrated sensing and communications (ISAC), the doubly-dispersive (DD) channel structure has long been heralded as a useful tool enabling the capture of the most important fading effects undergone by an arbitrary time-domain transmit signal propagating through some medium. However, the incorporation of this model into multiple-input multiple-output (MIMO) system setups, relying on the recent paradigm-shifting transceiver architecture based on stacked intelligent metasurfaces (SIM), in an environment with reconfigurable intelligent surfaces (RISs) remains an open problem due to the many intricate details that have to be accounted for. In this paper, we fill this gap by introducing a novel DD MIMO channel model that incorporates an arbitrary number of RISs in the ambient, as well as SIMs equipping both the transmitter and receiver. We then discuss how the proposed metasurfaces-parametrized DD (MPDD) channel model can be seamlessly applied to waveforms that are known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM), with each having their own inherent advantages and disadvantages. An illustrative application of the programmable functionality of the proposed model is finally presented to showcase its potential for boosting the performance of the aforementioned waveforms. Our numerical results indicate that the design of waveforms suitable to mitigating the effects of DD channels is significantly impacted by the emerging SIM technology.

eess.SP↗

arXiv2025

Communications-Centric Secure ISAC with Hybrid Reconfigurable Intelligent Surfaces

Hybrid reconfigurable intelligent surfaces (HRISs) constitute an emerging paradigm of metasurfaces that empowers the concept of smart wireless environments, inherently supporting simultaneously communications and sensing. Very recently, some preliminary HRIS designs for Integrated Sensing And Communications (ISAC) have appeared, however, secure ISAC schemes are still lacking. In this paper, we present a novel communications-centric secure ISAC framework capitalizing on the dual-functional capability of HRISs to realize bistatic sensing simultaneously with secure downlink communications. In particular, we jointly optimize the BS precoding vector and the HRIS reflection and analog combining configurations to enable simultaneous accurate estimation of both a legitimate user and an eavesdropper, while guaranteeing a predefined threshold for the secrecy spectral efficiency, with both operations focused within an area of interest. The presented simulation results validate the effectiveness of the proposed secure ISAC design, highlighting the interplay among key system design parameters as well as quantifying the trade-offs between the HRIS's absorption and reflection coeffcients.

eess.SP↗

arXiv2025

Parametrized Stacked Intelligent Metasurfaces for Bistatic Integrated Sensing and Communications

We consider stacked intelligent metasurfaces (SIMs) as a tool to improve the performance of bistatic integrated sensing and communications (ISAC) schemes. To that end, we optimize the SIMs and design a radar parameter estimation (RPE) scheme aimed at enhancing radar sensing capabilities as well as communication performance under ISAC-enabling waveforms known to perform well in doubly-dispersive (DD) channels. The SIM optimization is done via a min-max problem formulation solved via steepest ascent with closed-form gradients, while the RPE is carried out via a compressed sensing-based probabilistic data association (PDA) algorithm. Our numerical results indicate that the design of waveforms suitable to mitigating the effects of DD channels is significantly impacted by the emerging SIM technology.

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arXiv2025

Evaluation of Switching Technologies for Reflective and Transmissive RISs at Sub-THz Frequencies

For the upcoming 6G wireless networks, reconfigurable intelligent surfaces are an essential technology, enabling dynamic beamforming and signal manipulation in both reflective and transmissive modes. It is expected to utilize frequency bands in the millimeter-wave and THz, which presents unique opportunities but also significant challenges. The selection of switching technologies that can support high-frequency operation with minimal loss and high efficiency is particularly complex. In this work, we demonstrate the potential of advanced components such as Schottky diodes, memristor switches, liquid metal-based switches, phase change materials, and RF-SOI technology in RIS designs as an alternative to overcome limitations inherent in traditional technologies in D-band (110-170 GHz).

cs.ET↗

arXiv2025

DMA Reception for Simultaneous Area-Wide Sensing and Multi-User Uplink Communications

The recent surge in deploying extremely large antenna arrays is expected to play a vital role in future sixth generation wireless networks, enabling advanced radar target localization with enhanced angular and range resolution. This paper focuses on the promising technology of Dynamic Metasurface Antennas (DMAs), integrating numerous sub-wavelength-spaced metamaterials within a single aperture, and presents a novel framework for designing its analog reception beamforming weights with the goal to optimize sensing performance within a spatial Area of Interest (AoI), while simultaneously guaranteeing desired multi-user uplink communication performance. We derive the Cramer-Rao Bound (CRB) with DMA-based reception for both passive and active radar targets lying inside the AoI, which is then used as the optimization objective for configuring the discrete tunable phases of the metamaterials. Capitalizing on the DMA partially-connected architecture, we formulate the design problem as convex optimization and present both direct CRB minimization approaches and low complexity alternatives using a lower-bound approximation. Simulation results across various scenarios validate the effectiveness of the proposed framework, showing it consistently outperforms existing state-of-the-art methods.

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arXiv2025

Tracking-Aided Multi-User MIMO Communications with Hybrid Reconfigurable Intelligent Surfaces

Hybrid Reconfigurable Intelligent Surfaces (HRISs) constitute a new paradigm that redefines smart metasurfaces, not only offering tunable reflections of incoming signals, but also incorporating signal reception and processing capabilities. In this paper, leveraging the simultaneous dual-functionality of HRISs, we propose a novel framework for tracking-aided multi-user Multiple-Input Multiple-Output (MIMO) communications. In particular, a joint design of the transmit multi-user precoding matrix together with the HRIS reflection and analog combining configurations is presented, with the objective to maximize the accuracy of position estimation of multiple mobile users while meeting their individual quality-of-service constraints for sensing-aided communications. The Cramer-Rao bound for the users' positioning parameters is derived together with a prediction approach based on the extended Kalman filter. Our simulation results showcase the efficacy of the proposed Integrated Sensing And Communications (ISAC) framework over various system configuration parameters.

eess.SP↗

arXiv2025

Distributed Intelligent Sensing and Communications for 6G: Architecture and Use Cases

The Distributed Intelligent Sensing and Communication (DISAC) framework redefines Integrated Sensing and Communication (ISAC) for 6G by leveraging distributed architectures to enhance scalability, adaptability, and resource efficiency. This paper presents key architectural enablers, including advanced data representation, seamless target handover, support for heterogeneous devices, and semantic integration. Two use cases illustrate the transformative potential of DISAC: smart factory shop floors and Vulnerable Road User (VRU) protection at smart intersections. These scenarios demonstrate significant improvements in precision, safety, and operational efficiency compared to traditional ISAC systems. The preliminary DISAC architecture incorporates intelligent data processing, distributed coordination, and emerging technologies such as Reconfigurable Intelligent Surfaces (RIS) to meet 6G's stringent requirements. By addressing critical challenges in sensing accuracy, latency, and real-time decision-making, DISAC positions itself as a cornerstone for next-generation wireless networks, advancing innovation in dynamic and complex environments.

eess.SP↗

arXiv2025

Wideband THz Multi-User Downlink Communications with Leaky Wave Antennas

Future wireless systems are envisioned to utilize the large spectra available at THz bands for wireless communications. Extremely massive multiple-input multiple-output (MIMO) antennas can be costly and power inefficient for wideband THz communications. An alternative antenna technology, which can achieve low cost and power efficient THz signaling, is based on leaky wave antennas (LWAs). In this paper, we explore the usage of the LWAs for wideband downlink multi-user THz communications. We propose a model for LWA-aided communication systems that faithfully captures the antenna operations. We that LWAs yield frequency-dependent beams, where the equivalent wideband channel induces dependence between angle, frequency, and spectral lobe width. We identify the LWAs inherent frequency-selective beamsteering capabilities as motivating multi-band THz communications that deviate from conventional orthogonal frequency-division, and employ non-identical subbands. Then, we propose an alternating optimization algorithm for jointly optimizing the LWA configuration along with the spectral division and power allocation to maximize the achievable sum-rate. Our numerical results show that a single LWA can generate diverse beampatterns, exhibiting performance comparable to costly fully digital MIMO. Interestingly, we demonstrate that allowing transmission with non-identical subbands leverages the characteristics of LWA-based channels compared to uniform division, yielding improved beamsteering that translate to higher rates.

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arXiv2025

Multi-User MISO with Stacked Intelligent Metasurfaces: A DRL-Based Sum-Rate Optimization Approach

Stacked intelligent metasurfaces (SIMs) represent a novel signal processing paradigm that enables over-the-air processing of electromagnetic waves at the speed of light. Their multi-layer architecture exhibits customizable computational capabilities compared to conventional single-layer reconfigurable intelligent surfaces and metasurface lenses. In this paper, we deploy SIM to improve the performance of multi-user multiple-input single-output (MISO) wireless systems through a low complexity manner with reduced numbers of transmit radio frequency chains. In particular, an optimization formulation for the joint design of the SIM phase shifts and the transmit power allocation is presented, which is efficiently tackled via a customized deep reinforcement learning (DRL) approach that systematically explores pre-designed states of the SIM-parametrized smart wireless environment. The presented performance evaluation results demonstrate the proposed method's capability to effectively learn from the wireless environment, while consistently outperforming conventional precoding schemes under low transmit power conditions. Furthermore, the implementation of hyperparameter tuning and whitening process significantly enhance the robustness of the proposed DRL framework.

cs.IT↗

arXiv2025

Active Reconfigurable Intelligent Surfaces: Circuit Modeling and Reflection Amplification Optimization

Reconfigurable Intelligent Surfaces (RISs) constitute a promising emerging technology that enables wireless systems to control the propagation environment to enhance diverse communication objectives. To mitigate double-fading attenuation in RIS-aided links, the paradigm of active metamaterials capable of amplifying their incident wave has emerged. In this paper, capitalizing on the inherent negative-resistance region of tunnel diodes, we propose their integration into each RIS unit element to enable RISs with reflection amplification entirely in the analog domain. We derive novel realistic phase-amplitude relationships and power constraints specific to this model, addressing gaps in the existing literature where amplitude limits are often chosen arbitrarily. This characterization of our active RIS unit elements is incorporated into two novel optimization frameworks targeting the spectral efficiency maximization of RIS-assisted Multiple-Input-Multiple-Output (MIMO) systems, which are solved via an one-step approach and an iterative Alternating Optimization (AO) method. The former approach is used to initialize the AO framework, enhancing both its performance and convergence. Our numerical investigations emphasize the importance of accurately modeling phase-amplitude dependencies, and provide key insights into the impact of RIS-induced noise as well as the trade-off between available power and the number of active elements.

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arXiv2025

Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks

In the Edge Inference (EI) paradigm, where a Deep Neural Network (DNN) is split across the transceivers to wirelessly communicate goal-defined features in solving a computational task, the wireless medium has been commonly treated as a source of noise. In this paper, motivated by the emerging technologies of Reconfigurable Intelligent Surfaces (RISs) and Stacked Intelligent Metasurfaces (SIM) that offer programmable propagation of wireless signals, either through controllable reflections or diffractions, we optimize the RIS/SIM-enabled smart wireless environment as a means of over-the-air computing, resembling the operations of DNN layers. We propose a framework of Metasurfaces-Integrated Neural Networks (MINNs) for EI, presenting its modeling, training through a backpropagation variation for fading channels, and deployment aspects. The overall end-to-end DNN architecture is general enough to admit RIS and SIM devices, through controllable reconfiguration before each transmission or fixed configurations after training, while both channel-aware and channel-agnostic transceivers are considered. Our numerical evaluation showcases metasurfaces to be instrumental in performing image classification under link budgets that impede conventional communications or metasurface-free systems. It is demonstrated that our MINN framework can significantly simplify EI requirements, achieving near-optimal performance with $50~$dB lower testing signal-to-noise ratio compared to training, even without transceiver channel knowledge.

cs.LG↗

arXiv2025

Optimal One- and Two-Sided Multi-Level ASK for Noncoherent SIMO Systems Over Correlated Rician Fading

This paper analyzes the performance of a single-input multiple-output (SIMO) wireless communication system employing one- and two-sided amplitude shift keying (ASK) modulation schemes for data transmission and operating under correlated Rician fading channels. The receiver deploys an optimal noncoherent maximum likelihood detector, which exploits statistical knowledge of the channel state information for signal decoding. An optimal receiver structure is derived, from which series-form and closed-form expressions for the union bound on the symbol error probability (SEP) are obtained for general and massive SIMO systems, respectively. Furthermore, an optimization framework to derive the optimal one- and two-sided ASK modulation schemes is proposed, which focuses on minimizing SEP performance under an average transmit energy constraint. The conducted numerical investigations for various system parameters demonstrate that the proposed noncoherent SIMO system with the designed optimal ASK modulation schemes achieves superior error performance compared to traditional equispaced ASK modulation. It is also shown that, when the proposed system employs traditional two-sided ASK modulation, superior error performance from the case of using one-sided ASK is obtained.

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arXiv2025

Multi-Agent Deep Reinforcement Learning for Safe Autonomous Driving with RICS-Assisted MEC

Environment sensing and fusion via onboard sensors are envisioned to be widely applied in future autonomous driving networks. This paper considers a vehicular system with multiple self-driving vehicles that is assisted by multi-access edge computing (MEC), where image data collected by the sensors is offloaded from cellular vehicles to the MEC server using vehicle-to-infrastructure (V2I) links. Sensory data can also be shared among surrounding vehicles via vehicle-to-vehicle (V2V) communication links. To improve spectrum utilization, the V2V links may reuse the same frequency spectrum with V2I links, which may cause severe interference. To tackle this issue, we leverage reconfigurable intelligent computational surfaces (RICSs) to jointly enable V2I reflective links and mitigate interference appearing at the V2V links. Considering the limitations of traditional algorithms in addressing this problem, such as the assumption for quasi-static channel state information, which restricts their ability to adapt to dynamic environmental changes and leads to poor performance under frequently varying channel conditions, in this paper, we formulate the problem at hand as a Markov game. Our novel formulation is applied to time-varying channels subject to multi-user interference and introduces a collaborative learning mechanism among users. The considered optimization problem is solved via a driving safety-enabled multi-agent deep reinforcement learning (DS-MADRL) approach that capitalizes on the RICS presence. Our extensive numerical investigations showcase that the proposed reinforcement learning approach achieves faster convergence and significant enhancements in both data rate and driving safety, as compared to various state-of-the-art benchmarks.

cs.LG↗

arXiv2025

Minimum Mean Squared Error Holographic Beamforming for Sum-Rate Maximization

This paper studies the problem of hybrid holographic beamforming for sum-rate maximization in a communication system assisted by a reconfigurable holographic surface. Existing methodologies predominantly rely on gradient-based or approximation techniques necessitating iterative optimization for each update of the holographic response, which imposes substantial computational overhead. To address these limitations, we establish a mathematical relationship between the mean squared error (MSE) criterion and the holographic response of the RHS to enable alternating optimization based on the minimum MSE (MMSE). Our analysis demonstrates that this relationship exhibits a quadratic dependency on each element of the holographic beamformer. Exploiting this property, we derive closed-form optimal expressions for updating the holographic beamforming weights. Our complexity analysis indicates that the proposed approach exhibits only linear complexity in terms of the RHS size, thus, ensuring scalability for large-scale deployments. The presented simulation results validate the effectiveness of our MMSE-based holographic approach, providing useful insights.

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arXiv2025

Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems

Multi-Agent Deep Reinforcement Learning (MADRL) has emerged as a powerful tool for optimizing decentralized decision-making systems in complex settings, such as Dynamic Spectrum Access (DSA). However, deploying deep learning models on resource-constrained edge devices remains challenging due to their high computational cost. To address this challenge, in this paper, we present a novel sparse recurrent MARL framework integrating gradual neural network pruning into the independent actor global critic paradigm. Additionally, we introduce a harmonic annealing sparsity scheduler, which achieves comparable, and in certain cases superior, performance to standard linear and polynomial pruning schedulers at large sparsities. Our experimental investigation demonstrates that the proposed DSA framework can discover superior policies, under diverse training conditions, outperforming conventional DSA, MADRL baselines, and state-of-the-art pruning techniques.

cs.LG↗

arXiv2025

Integrating Sensing and Communications in 6G? Not Until It Is Secure to Do So

Integrated Sensing and Communication (ISAC) is emerging as a cornerstone technology for forthcoming 6G systems, significantly improving spectrum and energy efficiency. However, the commercial viability of ISAC hinges on addressing critical challenges surrounding security, privacy, and trustworthiness. These challenges necessitate an end-to-end framework to safeguards both communication data and sensing information, particularly in ultra-low-latency and highly connected environments. Conventional solutions, such as encryption and key management, often fall short when confronted with ISAC's dual-functional nature. In this context, the physical layer plays a pivotal role: this article reviews emerging physical-layer strategies, including artificial noise (AN) injection, cooperative jamming, and constructive interference (CI), which enhance security by mitigating eavesdropping risks and safeguarding both communication data and sensing information. We further highlight the unique privacy issues that ISAC introduces to cellular networks and outline future research directions aimed at ensuring robust security and privacy for efficient ISAC deployment in 6G.

cs.IT↗

arXiv2025

Graph-CNNs for RF Imaging: Learning the Electric Field Integral Equations

Radio-Frequency (RF) imaging concerns the digital recreation of the surfaces of scene objects based on the scattered field at distributed receivers. To solve this difficult inverse scattering problems, data-driven methods are often employed that extract patterns from similar training examples, while offering minimal latency. In this paper, we first provide an approximate yet fast electromagnetic model, which is based on the electric field integral equations, for data generation, and subsequently propose a Deep Neural Network (DNN) architecture to learn the corresponding inverse model. A graph-attention backbone allows for the system geometry to be passed to the DNN, where residual convolutional layers extract features about the objects, while a UNet head performs the final image reconstruction. Our quantitative and qualitative evaluations on two synthetic data sets of different characteristics showcase the performance gains of thee proposed advanced architecture and its relative resilience to signal noise levels and various reception configurations.

cs.LG↗

arXiv2025

Fluid Reconfigurable Intelligent Surfaces: Joint On-Off Selection and Beamforming with Discrete Phase Shifts

This letter proposes a fluid reconfigurable intelligent surface (FRIS) paradigm, extending the conventional reconfigurable intelligent surface (RIS) technology to incorporate position reconfigurability of the elements. In our model, a `fluid' element is realized by a dense matrix of subelements over a given space and dynamically selecting specific elements for signal modulation based on channel conditions. Specifically, we consider a FRIS-assisted single-user single-input single-output (SU-SISO) system and formulate an optimization problem that can jointly optimize element selection and their discrete phase shifts to maximize the achievable rate. To address this problem efficiently, we propose an iterative algorithm based on the cross-entropy optimization (CEO) framework. Simulation results reveal that FRIS achieves significant performance gains over traditional RIS.

cs.IT↗