arXiv subjects

Symeon Chatzinotas

Publications and source records attributed to Symeon Chatzinotas.

At least 109 records · Page 6Linked to original sources

arXiv2025

Security Analysis of MDI-QKD in Turbulent Free-Space Polarization Channels-A Composite Channel Framework

Atmospheric turbulence poses a significant challenge to free-space measurement-device-independent quantum key distribution (FSO MDI-QKD) by inducing polarization decoherence and depolarization, which degrade the secret key rate (SKR). In this paper, we propose a unified depolarizing-dephasing channel model for turbulence-induced polarization decoherence in FSO MDI-QKD. This model consolidates phase perturbations, Gaussian beam spreading, beam drift, aperture truncation, and scintillation into closed-form parameters: depolarization factor, decoherence factor, and detection probability. By mapping turbulence to a von Mises-Fisher/Watson-distributed SU(2) rotation, we derive an analytic SKR expression compatible with existing MDI-QKD security analyses. The model excels in clear, overcast, and hazy weather conditions, offering computational efficiency and experimental verifiability for real-time link adaptation. Numerical simulations, illustrated on a ground-to-satellite free-space link, confirm its accuracy, enabling robust physical layer design for global-scale MDI-QKD networks.

quant-ph↗

arXiv2025

SC-GIR: Goal-oriented Semantic Communication via Invariant Representation Learning

Goal-oriented semantic communication (SC) aims to revolutionize communication systems by transmitting only task-essential information. However, current approaches face challenges such as joint training at transceivers, leading to redundant data exchange and reliance on labeled datasets, which limits their task-agnostic utility. To address these challenges, we propose a novel framework called Goal-oriented Invariant Representation-based SC (SC-GIR) for image transmission. Our framework leverages self-supervised learning to extract an invariant representation that encapsulates crucial information from the source data, independent of the specific downstream task. This compressed representation facilitates efficient communication while retaining key features for successful downstream task execution. Focusing on machine-to-machine tasks, we utilize covariance-based contrastive learning techniques to obtain a latent representation that is both meaningful and semantically dense. To evaluate the effectiveness of the proposed scheme on downstream tasks, we apply it to various image datasets for lossy compression. The compressed representations are then used in a goal-oriented AI task. Extensive experiments on several datasets demonstrate that SC-GIR outperforms baseline schemes by nearly 10%,, and achieves over 85% classification accuracy for compressed data under different SNR conditions. These results underscore the effectiveness of the proposed framework in learning compact and informative latent representations.

cs.LG↗

arXiv2025

Demonstrator Testbed for Effective Precoding in MEO Multibeam Satellites

The use of communication satellites in medium Earth orbit (MEO) is foreseen to provide quasi-global broadband Internet connectivity in the coming networking ecosystems. Multi-user multiple-input single-output (MU-MISO) digital signal processing techniques, such as precoding, emerge as appealing technological enablers in the forward link of multi-beam satellite systems operating in full frequency reuse (FFR). However, the orbit dynamics of MEO satellites pose additional challenges that must be carefully evaluated and addressed. This work presents the design of an in-lab testbed based on software-defined radio (SDR) platforms and the corresponding adaptations required for efficient precoding in a MEO scenario. The setup incorporates a precise orbit model and the radiation pattern of a custom-designed direct radiating array (DRA). We analyze the main impairments affecting precoding performance, including Doppler shifts and payload phase noise, and propose a synchronization loop to mitigate these effects. Preliminary experimental results validate the feasibility and effectiveness of the proposed solution.

eess.SP↗

arXiv2025

In-Lab Carrier Aggregation Testbed for Satellite Communication Systems

Carrier Aggregation (CA) is a technique used in 5G and previous cellular generations to temporarily increase the data rate of a specific user during peak demand periods or to reduce carrier congestion. CA is achieved by combining two or more carriers and providing a virtual, wider overall bandwidth to high-demand users of the system. CA was introduced in the 4G/LTE wireless era and has been proven effective in 5G as well, where it is said to play a significant role in efficient network capacity management. Given this success, the satellite communication (SatCom) community has put its attention into CA and the potential benefits it can bring in terms of better spectrum utilization and better meeting the user traffic demand. While the theoretical evaluation of CA for SatCom has already been presented in several works, this article presents the design and results obtained with an experimentation testbed based on Software Defined Radio (SDR) and a satellite channel emulator. We first present the detailed implementation design, which includes a Gateway (GW) module responsible for PDU-scheduling across the aggregated carriers, and a User Terminal (UT) module responsible for aggregating the multiple received streams. The second part of the article presents the experimental evaluation, including CA over a single Geostationary (GEO) satellite, CA over a single Medium Earth Orbit (MEO) satellite, and CA combining carriers sent over GEO and MEO satellites. A key contribution of this work is the explicit consideration of multi-orbit scenarios in the testbed design and validation. The testing results show promising benefits of CA over SatCom systems, motivating potential upcoming testing on over-the-air systems.

eess.SP↗

arXiv2025

Secure Communications for All Users in Low-Resolution IRS-aided Systems Under Imperfect and Unknown CSI

Provisioning secrecy for all users, given the heterogeneity and uncertainty of their channel conditions, locations, and the unknown location of the attacker/eavesdropper, is challenging and not always feasible. This work takes the first step to guarantee secrecy for all users where a low resolution intelligent reflecting surfaces (IRS) is used to enhance legitimate users' reception and thwart the potential eavesdropper (Eve) from intercepting. In real-life scenarios, due to hardware limitations of the IRS' passive reflective elements (PREs), the use of a full-resolution (continuous) phase shift (CPS) is impractical. In this paper, we thus consider a more practical case where the phase shift (PS) is modeled by a low-resolution (quantized) phase shift (QPS) while addressing the phase shift error (PSE) induced by the imperfect channel state information (CSI). To that end, we aim to maximize the minimum secrecy rate (SR) among all users by jointly optimizing the transmitter's beamforming vector and the IRS's passive reflective elements (PREs) under perfect/imperfect/unknown CSI. The resulting optimization problem is non-convex and even more complicated under imperfect/unknown CSI. The resulting optimization problem is non-convex and even more complicated under imperfect/unknown CSI. To tackle it, we linearize the objective function and decompose the problem into sequential subproblems. When the perfect CSI is not available, we use the successive convex approximation (SCA) approach to transform imperfect CSI related semi-infinite constraints into finite linear matrix inequalities (LMI). We prove that our proposed algorithm converges to a locally optimal solution with low computational complexity. Extensive simulations with practical settings show that our approach can ensure secure communication for all users while the IRS's PREs are quantized and are affected by the PSE.

eess.SP↗

arXiv2025

Robust Design of Beyond-Diagonal Reconfigurable Intelligent Surface Empowered RSMA-SWIPT System Under Channel Estimation Errors

This work explores the integration of rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), and beyond-diagonal reconfigurable intelligent surface (BD-RIS) to enhance the spectral-efficiency, energy-efficiency, coverage, and connectivity of future sixth-generation (6G) communication networks. Specifically, with a multiuser BD-RIS-empowered RSMA-SWIPT system, we jointly optimize the transmit precoding vectors, the common rate proportion of users, the power-splitting ratios, and scattering matrix of BD-RIS node, under the assumption of imperfect channel state information (CSI). Additionally, to better capture practical hardware behavior, we incorporate a nonlinear energy harvesting model under energy harvesting constraints. We design a robust optimization framework to maximize the system sum-rate, while explicitly accounting for the worst-case impact of CSI uncertainties. Further, we introduce an alternating optimization framework that partitions the problem into several blocks, which are optimized iteratively. More specifically, the transmit precoding vectors are optimized by reformulating the problem as a convex semidefinite programming through successive-convex approximation (SCA), whereas the power-splitting problem is solved using the MOSEK-enabled CVX toolbox. Subsequently, to optimize the scattering matrix of the BD-RIS, we first employ SCA to reformulate the problem into a convex form, and then design a manifold optimization strategy based on the Conjugate-Gradient method. Finally, numerical simulation results reveal that the proposed scheme provides significant performance improvements over existing benchmarks and demonstrates rapid convergence within a reasonable number of iterations.

eess.SP↗

arXiv2025

Optimal Interference Exploitation Waveform Design with Relaxed Block-Level Power Constraints

This paper investigates constructive interference (CI)-based waveform design for phase shift keying and quadrature amplitude modulation symbols under relaxed block-level power constraints in multi-user multiple-input single-output (MU-MIMO) communication systems. Existing linear CI-based precoding methods, including symbol-level precoding (SLP) and block-level precoding (BLP), suffer from performance limitations due to strict symbol-level power budgets or insufficient degrees of freedom over the block. To overcome these challenges, we propose a nonlinear waveform optimization framework that introduces additional optimization variables and maximizes the minimum CI metric across the transmission block. The optimal waveform is derived in closed form using the function and Karush Kuhn Tucker conditions, and the solution is explicitly expressed with respect to the dual variables. Moreover, the original problems are equivalently reformulated as tractable quadratic programming (QP) problems. To efficiently solve the derived QP problems, we develop an improved alternating direction method of multipliers (ADMM) algorithm by integrating a linear-time projection technique, which significantly enhances the computational efficiency. Simulation results demonstrate that the proposed algorithms substantially outperform the conventional CI-SLP and CI-BLP approaches, particularly under high-order modulations and large block lengths.

eess.SP↗

arXiv2025

DT-Aided Resource Management in Spectrum Sharing Integrated Satellite-Terrestrial Networks

The integrated satellite-terrestrial networks (ISTNs) through spectrum sharing have emerged as a promising solution to improve spectral efficiency and meet increasing wireless demand. However, this coexistence introduces significant challenges, including inter-system interference (ISI) and the low Earth orbit satellite (LSat) movements. To capture the actual environment for resource management, we propose a time-varying digital twin (DT)-aided framework for ISTNs incorporating 3D map that enables joint optimization of bandwidth (BW) allocation, traffic steering, and resource allocation, and aims to minimize congestion. The problem is formulated as a mixed-integer nonlinear programming (MINLP), addressed through a two-phase algorithm based on successive convex approximation (SCA) and compressed sensing approaches. Numerical results demonstrate the proposed method's superior performance in queue length minimization compared to benchmarks.

eess.SP↗

arXiv2025

The Impact of LoRA Adapters on LLMs for Clinical Text Classification Under Computational and Data Constraints

Fine-tuning Large Language Models (LLMs) for clinical Natural Language Processing (NLP) poses significant challenges due to domain gap, limited data, and stringent hardware constraints. In this study, we evaluate four adapter techniques-Adapter, Lightweight, TinyAttention, and Gated Residual Network (GRN) - equivalent to Low-Rank Adaptation (LoRA), for clinical note classification under real-world, resource-constrained conditions. All experiments were conducted on a single NVIDIA Quadro P620 GPU (2 GB VRAM, 512 CUDA cores, 1.386 TFLOPS FP32), limiting batch sizes to <8 sequences and maximum sequence length to 256 tokens. Our clinical corpus comprises only 580 000 tokens, several orders of magnitude smaller than standard LLM pre-training datasets. We fine-tuned three biomedical pre-trained LLMs (CamemBERT-bio, AliBERT, DrBERT) and two lightweight Transformer models trained from scratch. Results show that 1) adapter structures provide no consistent gains when fine-tuning biomedical LLMs under these constraints, and 2) simpler Transformers, with minimal parameter counts and training times under six hours, outperform adapter-augmented LLMs, which required over 1000 GPU-hours. Among adapters, GRN achieved the best metrics (accuracy, precision, recall, F1 = 0.88). These findings demonstrate that, in low-resource clinical settings with limited data and compute, lightweight Transformers trained from scratch offer a more practical and efficient solution than large LLMs, while GRN remains a viable adapter choice when minimal adaptation is needed.

cs.CL↗

arXiv2025

Deadline-Aware Joint Task Scheduling and Offloading in Mobile Edge Computing Systems

The demand for stringent interactive quality-of-service has intensified in both mobile edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks in these systems necessitates adherence to specific deadlines or achieving extremely low latency. To optimize task scheduling performance, existing research has mainly focused on reducing the number of late jobs whose deadlines are not met. However, the primary challenge with these methods lies in the total search time and scheduling efficiency. In this paper, we present the optimal job scheduling algorithm designed to determine the optimal task order for a given set of tasks. In addition, users are enabled to make informed decisions for offloading tasks based on the information provided by servers. The details of performance analysis are provided to show its optimality and low complexity with the linearithmic time O(nlogn), where $n$ is the number of tasks. To tackle the uncertainty of the randomly arriving tasks, we further develop an online approach with fast outage detection that achieves rapid acceptance times with time complexity of O(n). Extensive numerical results are provided to demonstrate the effectiveness of the proposed algorithm in terms of the service ratio and scheduling cost.

cs.DC↗

arXiv2025

QTCAJOSA: Low-Complexity Joint Offloading and Subchannel Allocation for NTN-Enabled IoMT

In this work, we consider the resource allocation problem for task offloading from Internet of Medical Things (IoMT) devices, to a non-terrestrial network. The architecture considers clusters of IoMT devices that offload their tasks to a dedicated unmanned aerial vehicle (UAV) serving as a multi-access edge computing (MEC) server, which can compute the task or further offload it to an available high-altitude platform station (HAPS) or to a low-earth orbit (LEO) satellite for remote computing. We formulate a problem that has as objective the minimization of the weighted sum delay of the tasks. Given the non-convex nature of the problem, and acknowledging that the complexity of the optimization algorithms impact their performance, we derive a low-complexity joint subchannel allocation and offloading decision algorithm with dynamic computing resource initialization, developed as a greedy heuristic based on convex optimization criteria. Simulations show the gain obtained by including the different non-terrestrial nodes against architectures without them.

eess.SY↗

arXiv2025

Improved Differential Evolution for Enhancing the Aggregated Channel Estimation of RIS-Aided Cell-Free Massive MIMO

Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks.

cs.IT↗

arXiv2025

Enhanced Throughput and Seamless Handover Solutions for Urban 5G-Vehicle C-Band Integrated Satellite-Terrestrial Networks

This paper investigates downlink transmission in 5G Integrated Satellite-Terrestrial Networks (ISTNs) supporting automotive users (UEs) in urban environments, where base stations (BSs) and Low Earth Orbit (LEO) satellites (LSats) cooperate to serve moving UEs over shared C-band frequency carriers. Urban settings, characterized by dense obstructions, together with UE mobility, and the dynamic movement and coverage of LSats pose significant challenges to user association and resource allocation. To address these challenges, we formulate a multi-objective optimization problem designed to improve both throughput and seamless handover (HO). Particularly, the formulated problem balances sum-rate (SR) maximization and connection change (CC) minimization through a weighted trade-off by jointly optimizing power allocation and BS-UE/LSat-UE associations over a given time window. This is a mixed-integer and non-convex problem which is inherently difficult to solve. To solve this problem efficiently, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) technique. Furthermore, we introduce a practical prediction-based algorithm capable of providing efficient solutions in real-world implementations. Especially, the simulations use a realistic 3D map of London and UE routes obtained from the Google Navigator application to ensure practical examination. Thanks to these realistic data, the simulation results can show valuable insights into the link budget assessment in urban areas due to the impact of buildings on transmission links under the blockage, reflection, and diffraction effects. Furthermore, the numerical results demonstrate the effectiveness of our proposed algorithms in terms of SR and the CC-number compared to the greedy and benchmark algorithms.

eess.SP↗

arXiv2025

Performance Analysis of MDI-QKD in Thermal-Loss and Phase Noise Channels

Measurement-device-independent quantum key distribution (MDI-QKD), enhances quantum cryptography by mitigating detector-side vulnerabilities. This study analyzes MDI-QKD performance in thermal-loss and phase noise channels, modeled as depolarizing and dephasing channels to capture thermal and phase noise effects. Based on this channel framework, we derive analytical expressions for Bell state measurement probabilities, quantum bit error rates (QBER), and secret key rates (SKR) of MDI-QKD. Our simulations reveal that SKR decreases exponentially with transmission distance, with performance further degraded by increasing thermal noise and phase noise, particularly under high thermal noise conditions. These findings offer insights into enhancing MDI-QKD's noise resilience, supporting secure key generation in practical, noisy environments.

quant-ph↗

arXiv2025

Crosstalk-Resilient Quantum MIMO for Scalable Quantum Communications

We address the challenge of crosstalk in quantum multiplexing -an obstacle to scaling throughput in quantum communication networks. Crosstalk arises when physically coupled quantum modes interfere, degrading signal fidelity. We propose a mitigation strategy based on encoding discrete-variable (DV) quantum information into continuous-variable (CV) bosonic modes using Gottesman-Kitaev-Preskill (GKP) codes. By analyzing the effect of mode-mixing interference, we show that under specific noise strength conditions, the interaction can be absorbed into a gauge subsystem that leaves the logical content intact. We provide rigorous conditions for perfect transmission in the ideal case, derive the structure of the output codes and prove the existence of a gauge-fixing decoder enabling recovery of the logical information. Numerical simulations under displacement Gaussian noise illustrate the fidelity behavior and rate-fidelity tradeoff. Our results establish a coding-theoretic foundation for crosstalk-resilient multiplexing in quantum networks.

quant-ph↗

arXiv2025

Low-Complexity Channel Estimation Protocol for Non-Diagonal RIS-Assisted Communications

Non-diagonal reconfigurable intelligent surfaces (RISs) offer enhanced wireless signal manipulation over conventional RIS by enabling the incident signal on any of its $M$ elements to be reflected from another element via an $M \times M$ switch array. To fully exploit this flexible configuration, the acquisition of individual channel state information (CSI) is essential. However, due to the passive nature of the RIS, cascaded channel estimation is performed, as the RIS itself lacks signal processing capabilities. This entails estimating the CSI for the $M \times M$ switch array cascaded channels, resulting in estimating $M^2$ coefficients, to identify the optimal configuration of the non-diagonal RIS that maximizes the channel gain. In this paper, we propose a low-complexity channel estimation protocol that substantially reduces the estimation overhead from the exhaustive $M^2$ coefficients to only $3M$ coefficients for both single-input single-output (SISO) and multiple-input single-output (MISO) systems. Specifically, a three-stage pilot-based protocol is proposed to estimate scaled versions of the user-RIS and RIS-base-station (BS) channels in the first two stages using the least square (LS) estimator and the commonly used ON/OFF protocol for conventional RIS. These scaled estimates enable the optimization of the switch array. In the third stage, the cascaded user-RIS-BS channels are estimated to enable efficient beamforming optimization. Complexity analysis shows that our proposed protocol significantly reduces the BS estimation complexity from $\mathcal{O}(NM^2)$ to $\mathcal{O}(NM)$, where $N$ is the number of BS antennas. This complexity is similar to the ON/OFF-based LS estimation for conventional diagonal RIS.

eess.SP↗

arXiv2025

Interference in Spectrum-Sharing Integrated Terrestrial and Satellite Networks: Modeling, Approximation, and Robust Transmit Beamforming

This paper investigates robust transmit (TX) beamforming from the satellite to user terminals (UTs), based on statistical channel state information (CSI). The proposed design specifically targets the mitigation of satellite-to-terrestrial interference in spectrum-sharing integrated terrestrial and satellite networks. By leveraging the distribution information of terrestrial UTs, we first establish an interference model from the satellite to terrestrial systems without shared CSI. Based on this, robust TX beamforming schemes are developed under both the interference threshold and the power budget. Two optimization criteria are considered: satellite weighted sum rate maximization and mean square error minimization. The former achieves a superior achievable rate performance through an iterative optimization framework, whereas the latter enables a low-complexity closed-form solution at the expense of reduced rate, with interference constraints satisfied via a bisection method. To avoid complex integral calculations and the dependence on user distribution information in inter-system interference evaluations, we propose a terrestrial base station position-aided approximation method, and the approximation errors are subsequently analyzed. Numerical simulations validate the effectiveness of our proposed schemes.

eess.SP↗

arXiv2025

Dynamic Beyond 5G and 6G Connectivity: Leveraging NTN and RIS Synergies for Optimized Coverage and Capacity in High-Density Environments

The increasing demand for reliable, high-capacity communication during large-scale outdoor events poses significant challenges for traditional Terrestrial Networks (TNs), which often struggle to provide consistent coverage in high-density environments. This paper presents a novel 6G radio network planning framework that integrates Non-Terrestrial Networks (NTNs) with Reconfigurable Intelligent Surfaces (RISs) to deliver ubiquitous coverage and enhanced network capacity. Our framework overcomes the limitations of conventional deployable base stations by leveraging NTN architectures, including Low Earth Orbit (LEO) satellites and passive RIS platforms seamlessly integrated with Beyond 5G (B5G) TNs. By incorporating advanced B5G technologies such as Massive Multiple Input Multiple Output (mMIMO) and beamforming, and by optimizing spectrum utilization across the C, S, and Ka bands, we implement a rigorous interference management strategy based on a dynamic SINR model. Comprehensive calculations and simulations validate the proposed framework, demonstrating significant improvements in connectivity, reliability, and cost-efficiency in crowded scenarios. This integration strategy represents a promising solution for meeting the evolving demands of future 6G networks.

cs.NI↗