arXiv subjects

Alexandros-Apostolos A. Boulogeorgos

Publications and source records attributed to Alexandros-Apostolos A. Boulogeorgos.

At least 19 recordsLinked to original sources

arXiv2026

Brain-Inspired User Positioning for mmWave Beam Management

Wireless positioning is a key enabling functionality of high-frequency, highly directional wireless systems. Supporting beam tracking depends on accurately translating channel state in- formation (CSI) estimations into position predictions. However, as urban propagation environments are inherently highly complex, determining the process that connects the CSI with the mobile user position is a difficult task. Motivated by this observation, a great amount of effort was put on designing user positioning schemes that employ conventional machine learning approaches, such as k-nearest neighbors (k-NN), and convolutional neural networks (CNNs). However, conventional models achieve low energy efficiency (EE). To counterbalance this disadvantage, brain-inspired neural processing units (NPUs) has recently been introduced. For their optimum operation, NPUs require the execution of a new type of neural models, namely spiking neural networks (SNNs). Inspired by this, in this paper, we present a wireless network architecture that enables CSI acquisition, translation into mobile user position information, and beamforming adaptation through novel convolutional SNN (CSNN) models. To train and assess the models' performance, we created datasets based on realistic ray-tracing-based simulations. We applied the CSNN architecture and compared its performance against fingerprinting k-NNs, and CNNs in terms of both computing- and communication-tailored performance metrics. The results highlight a trade-off between EE and user positioning accuracy.

eess.SP↗

arXiv2026

On the Performance of THz Wireless Systems over $α$-$\mathcal{F}$ Channels with Beam Misalignment, Mobility and Hardware Impairments

This paper investigates the performance of terahertz (THz) wireless systems over the $α$-$\mathcal{F}$ fading channels with beam misalignment, mobility and hardware impairments. New expressions are derived for the probability density, cumulative distribution, and higher-order moments of the instantaneous signal-to-noise ratio (SNR). Building upon the aforementioned expressions, we extract novel formulas for the outage probability (OP), average symbol error probability, and average channel capacity. Asymptotic expressions are also derived, providing useful insights into system performance in the high-SNR regime. Furthermore, an upper bound on the capacity metric is obtained. Monte Carlo simulation results are presented to validate the developed analytical framework.

eess.SP↗

arXiv2026

Reliable ORIS-assisted FSO Communications via HARQ

This paper studies a free-space optical (FSO) link assisted by an optical reconfigurable intelligent surface (ORIS) and enhanced by a hybrid automatic repeat request (HARQ) scheme. The ORIS creates a virtual line-of-sight path around obstacles, while HARQ recovers frames corrupted by turbulence, pointing jitter, and geometric loss through retransmission and combining. We first derive a tractable statistical model for the end-to-end transmitter-ORIS-receiver (Tx-ORIS-Rx) reflected channel by jointly accounting for atmospheric turbulence, ORIS-induced pointing errors, and geometric attenuation. Building on these results, we obtain closed-form outage probability (OP) expressions for HARQ with Chase combining (HARQ-CC) and analytical outage upper bounds for HARQ with incremental redundancy (HARQ-IR), valid for an arbitrary maximum number of transmission rounds. We further conduct a high signal-to-noise ratio (SNR) analysis that provides a thorough characterization of the outage behavior and reveals the diversity order of both schemes. In addition, we characterize the delay behavior of the truncated HARQ process through the mean number of transmission rounds and the conditional mean number of rounds given successful decoding. Finally, numerical and Monte Carlo results validate the proposed analysis and show that HARQ substantially improves ORIS-assisted FSO reliability, with HARQ-IR achieving lower outage and delay than HARQ-CC, even for a small number of retransmission rounds.

eess.SP↗

arXiv2026

Physical-AI: From Channel Awareness to Environmental Intelligence in 6G Wireless Networks

Conventional wireless networks rely on instantaneous channel state information (CSI) and react to channel variations without explicitly modeling the physical environment, limiting their ability to handle blockage, mobility, and interference in dynamic deployments. Paradigms such as Integrated Sensing and Communication (ISAC) add sensing capabilities but lack explicit environment modeling and decision-making. In this article, we propose Physical-AI: a new architecture for environment-aware wireless networking, where radio signals enable sensing, modeling, and interaction with the environment in addition to data transmission. The framework proposes a self-supervised spatiotemporal radio foundation model for transforming distributed radio observations into a shared latent environmental representation. Multiple inference heads operate on this representation to estimate key environmental properties, including blockage, user distribution, mobility dynamics, and interference structure. A task-specific neural decision layer maps this representation to proactive, context-aware control actions. By integrating perception, world modeling, and decision-making in a closed loop, the proposed framework goes beyond ISAC and establishes Physical-AI as a promising architecture for intelligent 6G systems. Simulation results show that the proposed predictive framework reduces outage probability and blockage-response latency, particularly under increasing beam-switching delays.

cs.NI↗

arXiv2026

Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization

HandOver (HO) control in cellular networks is governed by a set of HO control parameters that are traditionally configured through rule-based heuristics. A key parameter for HO optimization is the Cell Individual Offset (CIO), defined for each pair of neighboring cells and used to bias HO triggering decisions. At network scale, tuning CIOs becomes a tightly coupled problem: small changes can redirect mobility flows across multiple neighbors, and static rules often degrade under non-stationary traffic and mobility. We exploit the pairwise structure of CIOs by formulating HO optimization as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) on the network's dual graph. In this representation, each agent controls a neighbor-pair CIO and observes Key Performance Indicators (KPIs) aggregated over its local dual-graph neighborhood, enabling scalable decentralized decisions while preserving graph locality. Building on this formulation, we propose TD3-D-MA, a discrete Multi-Agent Reinforcement Learning (MARL) variant of the TD3 algorithm with a shared-parameter Graph Neural Network (GNN) actor operating on the dual graph and region-wise double critics for training, improving credit assignment in dense deployments. We evaluate TD3-D-MA in an ns-3 system-level simulator configured with real-world network operator parameters across heterogeneous traffic regimes and network topologies. Results show that TD3-D-MA improves network throughput over standard HO heuristics and centralized RL baselines, and generalizes robustly under topology and traffic shifts.

cs.NI↗

arXiv2026

Secure and Green RSMA-Assisted Heterogeneous ISAC

This paper investigates sensing, communication, security, and energy efficiency of the heterogeneous integrated sensing and communication networks under challenging operational conditions. We focus on scenarios in which communication performance, security, and sensing accuracy are degraded by interference, eavesdropping, and imperfect channel state information. To this end, we analyze communication and sensing signals within ISAC framework as well as the communication signals of a multicast network based on \emph{rate-splitting multiple access} (RSMA). Then, sensing signal-to-cluster-plus-noise ratio, communication rate, security rate, and \emph{security energy efficiency} (SEE) are evaluated. To simultaneously enhance these system performances, we propose a targeted optimization framework aimed at maximizing SEE. This framework characterizes the sensing-security trade-off by jointly optimizing the transmit \emph{beamforming} (BF) vectors and the echo BF vector to construct green interference using the echo signal, as well as common and private streams generated by RSMA. Particularly, the joint design improves the security rate and reduces power consumption, thereby enabling a higher SEE. Given the non-convex nature of the optimization problem, we present an alternative approach that leverages Taylor series expansion, majorization-minimization, semi-definite programming, and successive convex approximation techniques. Specifically, we decompose the original non-convex and intractable optimization problem into three simplified sub-optimization problems, which are iteratively solved using an alternating optimization strategy. Simulations provide comparisons with state-of-the-art schemes, highlighting the superior efficiency, robustness, and scalability of the proposed joint multi-BF optimization scheme based on RSMA and green interference in improving system performances.

cs.IT↗

arXiv2026

Waveform Design for Partial-Time Superimposed ISAC Systems

Nowadays, waveforms of integrated sensing and communication (ISAC) are almost based on conventional communication and sensing signal, which bounds both the communication and sensing performance. To deal with this issue, in this paper, a novel waveform design is presented for the partial-time superimposed (PTS) ISAC system. At the base station (BS), a parameter-adjustable linear frequency modulation (LFM) pulse signal and a continuous communication orthogonal frequency division multiplexing (OFDM) signal are employed to broadcast public information and perform sensing tasks, respectively, using a PTS scheme. Pulse compression gain enhances the system's long-range sensing capability, while OFDM ensures the system's high-speed data transmission capability. Meanwhile, the LFM signal is utilized as superimposed pilot for channel estimation, which has higher time-frequency resource utilization and stronger real-time performance compared to orthogonal pilots. We present an accurate parameter estimation method of multi-path sensing signal for reconstructing and interference cancellation in communication users. Additionally, a cyclic maximum likelihood method is introduced for channel estimation and the Cramer-Rao lower bound (CRLB) of channel estimation is derived. Simulations demonstrate the accuracy and robustness of the proposed parameter estimation algorithm as well as the improved channel estimation performance over traditional methods. The proposed waveform design method can achieve reliable data transmission and accurate target sensing.

eess.SP↗

arXiv2026

SORIS: A Self-Organized Reconfigurable Intelligent Surface Architecture for Wireless Communications

In this paper, a new reconfigurable intelligent surface (RIS) hardware architecture, called self-organized RIS (SORIS), is proposed. The architecture incorporates a microcontroller connected to a single-antenna receiver operating at the same frequency as the RIS unit elements, operating either in transmission or reflection mode. The transmitting RIS elements enable the low latency estimation of both the incoming and outcoming channels at the microcontroller's side. In addition, a machine learning approach for estimating the incoming and outcoming channels involving the remaining RIS elements operating in reflection mode is devised. Specifically, by appropriately selecting a small number of elements in transmission mode, and based on the channel reciprocity principle, the respective channel coefficients are first estimated, which are then fed to a low-complexity neural network that, leveraging spatial channel correlation over RIS elements, returns predictions of the channel coefficients referring to the rest of elements. In this way, the SORIS microcontroller acquires channel state information, and accordingly reconfigures the panel's metamaterials to assist data communication between a transmitter and a receiver, without the need for separate connections with them. Moreover, the impact of channel estimation on the proposed solution, and a detailed complexity analysis for the used model, as well as a wiring density and control signaling analysis, is performed. The feasibility and efficacy of the proposed self-organized RIS design and operation are verified by Monte Carlo simulations, providing useful guidelines on the selection of the RIS elements for operating in transmission mode for initial channel estimation.

eess.SP↗

arXiv2025

Analysis and Design of a Reconfigurable Metasurface based on Chalcogenide Phase-Change Material for Operation in the Near and Mid Infrared

We analyze, design and assess the performance of a reflective reconfigurable metasurface (MS) architecture for optical wireless communications. The device is based on the Ge-Sb-Te (GST225) phase-change material (PCM) alloy, thermally toggled between highly distinct amorphous and crystalline phase-states. We employ simple conductive MS patterns to tune its resonance frequency, while allowing the unit cell response to be analytically predicted using transmission line theory and equivalent circuits. The GST material dispersion is computed in its two extreme phase-states using a Drude/Tauc-Lorentz model (DTLM), whose parameters are fitted to state-of-the-art experimental data; the dispersion in intermediate partially crystallized phase-states is computed using the Lorentz-Lorenz formula. Our results, corroborated by full-wave simulations, demonstrate the potential of PCM materials for the implementation of continuously reconfigurable holographic metasurfaces operating in the infrared bands.

physics.optics↗

arXiv2025

Markov Chain-based Model of Blockchain Radio Access Networks

Security has always been a priority, for researchers, service providers and network operators when it comes to radio access networks (RAN). One wireless access approach that has captured attention is blockchain enabled RAN (B-RAN) due to its secure nature. This research introduces a framework that integrates blockchain technology into RAN while also addressing the limitations of state-of-the-art models. The proposed framework utilizes queuing and Markov chain theory to model the aspects of B-RAN. An extensive evaluation of the models performance is provided, including an analysis of timing factors and a focused assessment of its security aspects. The results demonstrate reduced latency and comparable security making the presented framework suitable for diverse application scenarios.

eess.SY↗

arXiv2025

Metaverse Framework for Wireless Systems Management

This article introduces a comprehensive metaverse framework, which is designed for the simulation, emulation, and interaction with wireless systems. The proposed framework integrates core metaverse technologies such as extended reality (XR), digital twins (DTs), artificial intelligence (AI), internet of things (IoT), blockchain, and advanced 6G networking solutions to create a dynamic, immersive platform for both system development and management. By leveraging XR, users can visualize and engage with complex systems, while DTs enable real-time monitoring and optimization. AI generates the three-dimensional (3D) content, enhances decision-making and system performance, whereas IoT devices provide real-time sensor data for boosting the simulation accuracy. Additionally, blockchain ensures secure, decentralized interactions, and 5G/6G networks offer the necessary infrastructure for seamless, low-latency communication. This framework serves as a robust tool for exploring, developing, and optimizing wireless systems, aiming to provide valuable insights into the future of networked environments.

cs.NI↗

arXiv2025

Simultaneous Information and Control Signalling Protocol for RIS-Empowered Wireless Systems

Integration of RIS in radio access networks requires signaling between edge units and the RIS microcontroller (MC). Unfortunately, in several practical scenarios, the signaling latency is higher than the communication channel coherence time, which causes outdated signaling at the RIS. To counterbalance this, we introduce a simultaneous information and control signaling (SICS) protocol that enables operation adaptation through wireless control signal transmission. SICS assumes that the MC is equipped with a single antenna that operates at the same frequency as the RIS. RIS operates in simultaneous transmission and reflection (STAR) mode, and the source employs non-orthogonal multiple access (NOMA) to superposition the information signal to the control signal. To maximize the achievable user data rate while ensuring the MC's ability to decode the control signal, we formulate and solve the corresponding optimization problem that returns RIS's reflection and transmission coefficients as well as the superposition coefficients of the NOMA scheme. Our results reveal the robustness of the SICS approach.

eess.SP↗

arXiv2025

Spiking Neural Networks for Resource Allocation in UAV-Enabled Wireless Networks

This work presents a new spiking neural network (SNN)-based approach for user equipment-base station (UE-BS) association in non-terrestrial networks (NTNs). With the introduction of UAV's in wireless networks, the system architecture becomes heterogeneous, resulting in the need for dynamic and efficient management to avoid congestion and sustain overall performance. The presented framework compares two SNN-based optimization strategies. Specifically, a top-down centralized approach with complete network visibility and a bottom-up distributed approach for individual network nodes. The SNN is based on leak integrate-and-fire neurons with temporal components, which can perform fast and efficient event-driven inference. Realistic ray-tracing simulations are conducted, which showcase that the bottom-up model attains over 90\% accuracy, while the top-down model maintains 80-100\% accuracy. Both approaches reveal a trade-off between individually optimal solutions and UE-BS association feasibility, thus revealing the effectiveness of both approaches depending on deployment scenarios.

eess.SP↗

arXiv2025

Dual-UAV-Aided Covert Communications for Air-to-Ground ISAC Networks

To enhance both the sensing and covert communication performance, a dual-unmanned aerial vehicle (UAV)-aided scheme is proposed for integrated sensing and communication networks, in which one UAV maneuvers as the aerial dual-functional base-station (BS), while another UAV flies as the cooperative jammer. Artificial noise (AN) transmitted by the jamming UAV is utilized not only to confuse the ground warden but also to aid the aerial BS to sense multiple ground targets by combing the target-echoed dual-functional waveform and AN components from a perspective of the hybrid monostatitc-bistatic radar. We employ the distance-normalized beampattern sum-gain to measure the sensing performance. To maximize the average covert rate (ACR) from the aerial BS to the ground user, the dual-functional BS beamforming, jamming UAV beamforming, and dual-UAV trajectory are co-designed, subject to transmit power budgets, UAV maneuver constraint, covertness requirement, and sensing performance constraint. The imperfect successive interference cancellation (SIC) effects on the received signal-to-interference-plus-noise ratio are also considered in maximizing the ACR. To tackle the highly complicated non-convex ACR maximization problem, dual-UAV beamforming and dual-UAV trajectory are optimized in a block coordinate descent way using the trust-region successive convex approximation and semidefinite relaxation. To find the dual-UAV maneuver locations suitable for sensing the ground targets, we first optimize the dual-UAV trajectory for the covert communication purpose only and then solve a weighted distance minimization problem for the covert communication and sensing purpose.

cs.IT↗

arXiv2025

Fairness vs. Equality: RSMA-Based Multi-Target and Multi-User Integrated Sensing and Communications

This paper investigates the tradeoff between sensing and communication in an ISAC system comprising multiple sensing targets and communication users. A dual-functional base station conducts downlink data transmission services based on RSMA for multiple users, while sensing surrounding multiple targets. To enable effective multicast communications and ensure fair and balanced multi-target sensing and under a constrained power budget, we propose a multi-target sensing enhancement scheme incorporating fairness-aware BF, common rate splitting, and sensing power allocation. The proposed scheme minimizes the sensing CRB, while maximizing communication rate demands. Specifically, we derive closed-form expressions for both sensing CRB and communication rates. Building upon them, we formulate an optimization problem aiming to minimize the sensing CRB, while maximizing the communication rates. Considering the non-convex nature of the original optimization problem poses significant computational challenges, we transform the tradeoff optimization into a Pareto-optimal problem by employing Taylor series expansion, semi-definite relaxation, successive convex approximation, and penalty function to transform the non-convex problem and associated constraints into tractable forms. Extensive simulations validate the theoretical analysis and demonstrate significant advantages of the proposed RSMA-based fairness-aware BF over non-orthogonal multiple access, space division multiple access, and orthogonal multiple access, through comprehensive comparisons in two key aspects: CRB performance improvement and sensing-communication tradeoff characteristics. The proposed optimization framework exhibits remarkable superiority in enhancing both sensing accuracy and communication quality for ISAC systems.

cs.IT↗

arXiv2025

Optical RIS-enabled Multiple Access Communications

In this paper, we identify optical reconfigurable intelligent surfaces (ORISs) as key enablers of next-generation free-space optical (FSO) multiple access systems. By leveraging their beam steering and beam splitting capabilities, ORISs are able to effectively address line-of-sight (LoS) constraints, while enabling multi-user connectivity. We consider an ORIS-assisted non-orthogonal multiple access (NOMA) system model consisting of a single transmitter (Tx) and two receivers (Rxs). We derive novel analytical expressions to characterize the statistical particularities of the Tx-ORIS-Rx communication channel. Building upon the aforementioned expressions, we investigate the outage performance of the Rxs by deriving exact analytical expressions for the outage probability (OP) of each Rx. To provide deeper insights into the impact of various system parameters and physical conditions on the outage performance of each Rx, we conduct a high signal-to-noise ratio (SNR) analysis, that returns asymptotic expressions for the Rxs OPs at the high-SNR regime. Monte Carlo simulations validate the analysis, demonstrate the effectiveness of ORIS-enabled NOMA under a variety of configurations and physical scenarios, and showcase its superiority over its orthogonal-based counterpart.

eess.SP↗

arXiv2025

Coalition Formation for Heterogeneous Federated Learning Enabled Channel Estimation in RIS-assisted Cell-free MIMO

Downlink channel estimation remains a significant bottleneck in reconfigurable intelligent surface-assisted cell-free multiple-input multiple-output communication systems. Conventional approaches primarily rely on centralized deep learning methods to estimate the high-dimensional and complex cascaded channels. These methods require data aggregation from all users for centralized model training, leading to excessive communication overhead and significant data privacy concerns. Additionally, the large size of local learning models imposes heavy computational demands on end users, necessitating strong computational capabilities that most commercial devices lack. To address the aforementioned challenges, a coalition-formation-guided heterogeneous federated learning (FL) framework is proposed. This framework leverages coalition formation to guide the formation of heterogeneous FL user groups for efficient channel estimation. Specifically, by utilizing a distributed deep reinforcement learning (DRL) approach, each FL user intelligently and independently decides whether to join or leave a coalition, aiming at improving channel estimation accuracy, while reducing local model size and computational costs for end users. Moreover, to accelerate the DRL-FL convergence process and reduce computational burdens on end users, a transfer learning method is introduced. This method incorporates both received reference signal power and distance similarity metrics, by considering that nodes with similar distances to the base station and comparable received signal power have a strong likelihood of experiencing similar channel fading. Massive experiments performed that reveal that, compared with the benchmarks, the proposed framework significantly reduces the computational overhead of end users by 16%, improves data privacy, and improves channel estimation accuracy by 20%.

cs.IT↗

arXiv2025

Covert Communications in Active-IOS Aided Uplink NOMA Systems With Full-Duplex Receiver

In this paper, an active intelligent omni-surface (A-IOS) is deployed to aid uplink transmissions in a non-orthogonal multiple access (NOMA) system. In order to shelter the covert signal embedded in the superposition transmissions, a multi-antenna full-duplex (FD) receiver is utilized at the base-station to recover signal in addition to jamming the warden. With the aim of maximizing the covert rate, the FD transmit and receive beamforming, A-IOS refraction and reflection beamforming, NOMA transmit power, and FD jamming power are jointly optimized. To tackle the non-convex covert rate maximization problem subject to the highly coupled system parameters, an alternating optimization algorithm is designed to iteratively solve the decoupled sub-problems of optimizing the system parameters. The optimal solutions for the sub-problems of the NOMA transmit power and FD jamming power optimizations are derived in closed-form. To tackle the rank-one constrained non-convex fractional programming of the A-IOS beamforming and FD beamforming, a penalized Dinkelbach transformation approach is proposed to resort to the optimal solutions via semidefinite programming. Numerical results clarify that the deployment of the A-IOS significantly improves the covert rate compared with the passive-IOS aided uplink NOMA system. It is also found that the proposed scheme provides better covert communication performance with the optimized NOMA transmit power and FD jamming power compared with the benchmark schemes.

cs.IT↗