arXiv subjects

Symeon Chatzinotas

Publications and source records attributed to Symeon Chatzinotas.

At least 19 recordsLinked to original sources

arXiv2026

Movable Beyond-Diagonal RIS for ISAC: Optimal Placement and Pareto Boundary

This letter investigates integrated sensing and communication using a movable beyond-diagonal reconfigurable intelligent surface (BD-RIS). Under a rank-one line-of-sight BS--RIS channel, we derive the exact communication--sensing Pareto boundary of a fully-connected BD-RIS in terms of user--target channel correlation. For line-of-sight RIS links, maximizing this correlation yields the optimal placement. We characterize the minimum track aperture that eliminates the illumination trade-off and show that a movable diagonal RIS then attains the same optimum. Below this threshold, a dynamic program offers certified placement accuracy. Numerical results examine hardware connectivity, angular mismatch, and Rician fading.

eess.SP↗

arXiv2026

QNETSIM for memory assisted design of satellite and hybrid quantum networks

Space-based quantum information networks require simulation tools that capture orbital dynamics, probabilistic quantum operations, and classical coordination. We introduce QNETSIM, a modular discrete-event simulator for the design and performance evaluation of satellite and hybrid space-ground quantum networks. The framework integrates orbital geometry, free-space optical and fiber channels, quantum memories, entanglement generation, and Bell-state measurements within a common event-driven architecture. Physical models account for optical loss, atmospheric turbulence, detector imperfections, finite memory capacity, and storage-induced decoherence. Space-time routing and segmented nesting support entanglement distribution across changing topologies, while a software-defined control plane coordinates generation, storage, and swapping. Configurable scenarios encompass onboard repeaters, satellite constellations, ground-based repeaters, and direct satellite distribution. Through explicit interfaces between physical models, network protocols, and orchestration, QNETSIM links component characteristics and operational policies to entanglement fidelity, throughput, latency, availability, and memory utilization, enabling systematic exploration of architectural and resource-management trade-offs in future space-based quantum information networks.

quant-ph↗

arXiv2026

Satellite Intelligence: Distributed Training, Collaborative Inference,and LLM Adaptation in LEO Constellation

The proliferation of orbital sensing data and the real-time demands of emerging global services are pushing low Earth orbit (LEO) satellite networks from bent-pipe relays toward intelligent onboard processing. This shift confronts a fundamental tension, where modern artificial intelligence (AI) training paradigms require intensive computation, abundant memory, and stable connectivity, all of which are scarce under the dynamic topologies, intermittent links, and severe size, weight, and power (SWaP) constraints of satellite networks. Addressing this tension demands rethinking AI workflows, from training and inference to the emerging deployment of large language models (LLMs), under a constraint-aware design philosophy. To the best of our knowledge, this paper provides the first survey that jointly covers all three, whereas prior surveys examine these aspects in isolation. We begin by identifying the deployment challenge of satellite intelligence (SI) and deriving design principles for SI. We then examine three advancing paradigms: i) distributed training, where federated learning (FL) and split learning (SL) enable satellite-ground co-training under resource limitations; ii) collaborative inference, which exploits single-satellite, multi-satellite, and satellite-ground architectures to meet real-time service demands; and iii) on-orbit LLM adaptation and serving, which tackles the frontier challenge of deploying LLMs under severe orbital constraints and resource-aware serving. Finally, we identify open challenges and future directions. By bridging the AI and satellite networks, this paper provides a research roadmap for the implementation of space-native AI technologies.

cs.NI↗

arXiv2026

Secure Integrated Sensing and Communications with Movable Beyond-Diagonal RIS

In integrated sensing and communication (ISAC), when the sensed target is itself an eavesdropper, the illumination required for sensing coincides with the leakage that secrecy must suppress. This paper shows how a beyond-diagonal reconfigurable intelligent surface (BD-RIS) with movable elements decouples the two tasks in a monostatic setting, where the echo returns to the base station through the surface. We prove that a lossless reciprocal BD-RIS maps $K$ incident fields onto $K$ prescribed fields if and only if the two sets share the same Gram matrix and their bilinear Gram matrix is symmetric; unlike a diagonal RIS, it can thus steer a two-dimensional incident subspace onto any prescribed subspace. With a single stream, sensing incurs an unavoidable rate loss, whereas a closed-form zero-forcing design with a confidential and a Gaussian sensing stream nulls the data at the target and the sensing at the user, so that sensing costs only an SNR offset. Since all variables follow in closed form for any placement, the joint design reduces to a monotone search over the element positions, without alternating optimization. Simulations show that the dual-stream design loses only $1.1$ bit/s/Hz when the sensing requirement grows by $30$ dB, against $8.3$ bit/s/Hz for a single stream, while movement, interconnection, and the robust derivative-null design add up to $1.9$, $0.9$, and $2.6$ bit/s/Hz, respectively.

eess.SP↗

arXiv2026

Space-Time Stacked Intelligent Metasurfaces for Doppler-Resilient LEO Broadcasting

Stacked intelligent metasurfaces (SIMs) cascade programmable layers in front of a feed. In the SIM designs reported so far, including those proposed for low-Earth-orbit (LEO) satellites, the layer coefficients are space-only controls or are varied randomly in time to support opportunistic scheduling, so that every radiated beam carries the frequency of the feed. This paper introduces space-time SIMs (ST-SIMs), whose layers are modulated in time and use the temporal dimension of the stack deterministically: each spatial mode of the aperture is paired with its own prescribed frequency translation. The application is LEO broadcasting, where a common waveform reaches terminals with different Doppler shifts, and any common Doppler correction leaves residuals of up to half the Doppler span. We derive the harmonic-domain model of the stack, solve the minimum-energy space-frequency synthesis in closed form, and prove that full column rank of the steering matrix is necessary and sufficient for decoupling K terminal-specific translations, a result that extends to groups of terminals. We further propose a phase-only synthesis algorithm with guaranteed stationarity, together with quantization and control-rate laws. Simulations at 20 GHz over 50 random drops show that a four-layer ST-SIM with a single radio-frequency chain removes the Doppler floor of a static SIM for footprint radii above about 45 km and, under the ideal phase-only model and at equal radiated peak energy, approaches ideal digital per-terminal precompensation within 0.5 dB for resolvable terminals, whereas, in the evaluated geometries, a single time-modulated layer cannot synthesize the required modes.

eess.SP↗

arXiv2026

Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization

While Mixture-of-Experts (MoE) models effectively scale model capacity through sparse activation, their deployment is often bottlenecked by prohibitive memory requirements. Extracting a compact subset of experts presents a promising solution. However, existing expert selection heuristics predominantly rely on Top-$k$ ranking, which isolates the evaluation of individual experts and ignores the intricate inter-expert dependencies introduced by the MoE gating network. In this paper, we propose DS-MoE, a theoretically grounded framework that redefines expert selection via difference-of-submodular (DS) optimization. By analyzing the second-order Taylor expansion of the loss degradation, we reveal functional duality within expert combinations: redundancy (where experts encode overlapping representations) and synergy (where experts provide complementary error cancellation). To navigate this duality, we mathematically decouple redundancy reduction from synergy maximization by formulating the selection objective as a DS function. Furthermore, we devise a tailored majorization-minimization (MM) algorithm with provable guarantees to efficiently identify a high-quality expert subset. Extensive experiments demonstrate that DS-MoE effectively preserves indispensable expert combinations, achieving superior performance compared to the state-of-the-art baselines.

cs.LG↗

arXiv2026

Handover-Aware Power Minimization for Networked LEO Satellite Communications: Joint Cooperative Beamforming and Scheduling

Networked low Earth orbit (LEO) satellite constellations enabled by inter-satellite links offer a promising path toward ubiquitous broadband non-terrestrial services, as cooperative transmission increases the received power and suppresses interference. However, fast orbital motion induces frequent handovers, which cost energy and transmission time, while the limited number of radio-frequency chains per satellite couples user scheduling with cooperative beamforming. In this paper, we investigate handover-aware power-efficient downlink transmission in networked LEO systems under statistical channel state information. Specifically, we first introduce a two-segment frame structure that separates the handover procedure from user-plane transmission, and propose a power consumption model that captures the energy to establish each new satellite-user link and the transmission time lost during its handover. We then formulate a per-frame network-wide power minimization problem that jointly optimizes cooperative beamforming and user scheduling, and develop an iterative algorithm that combines a reweighted $\ell_2$ surrogate with a penalty-based relaxation and a fractional-programming inner loop, which yields convex subproblems. Simulation results with Red Sea ship traffic demonstrate that, at $25$~Mbps per user, the proposed design needs 14\% less power than distance-based cooperative scheduling and 15\% less than a joint design that ignores the established links, while non-cooperative transmission meets the rate requirements in only 14\% of the frames.

eess.SP↗

arXiv2026

Leveraging Movable-Element STARS for RSMA-SWIPT Under Practical System Imperfections

We study a robust multiuser framework for simultaneous wireless information and power transfer (SWIPT), where rate-splitting multiple access (RSMA) is integrated with a movable-element simultaneously transmitting and reflecting surface (ME-STARS). By allowing the STARS elements to change their positions in addition to controlling the reflection and transmission coefficients, the proposed architecture provides additional spatial flexibility for improving the cascaded channels toward users in both regions. Meanwhile, RSMA is employed to manage multiuser interference, while power-splitting receivers enable simultaneous information decoding and energy harvesting. The system design further accounts for channel state information (CSI) uncertainty, residual transceiver hardware impairments (HIs), and the nonlinear characteristics of practical energy-harvesting circuits. Accordingly, a robust sum-rate maximization problem is formulated by jointly designing the BS precoders, common-rate allocation, ME-STARS reflection/transmission coefficients, power-splitting (PS) ratios, and movable-element positions. The resulting formulation is highly non-convex because the movable-element positions affect the cascaded channels non-linearly and are tightly coupled with the remaining design variables. To obtain a tractable solution, the joint design is decomposed into active beamforming, passive beamforming, movable-element positioning, and PS-ratio optimization blocks, which are updated iteratively through suitable convex reformulations. Numerical results show that the proposed framework delivers higher sum rates than the considered benchmark schemes, exhibits robust behavior under CSI uncertainty and residual HIs, and achieves stable convergence across the considered system configurations.

eess.SP↗

arXiv2026

Physics-Driven Framework for QoS-Constrained Satellite System with Non-Linear PA Distortion

This work develops a physics-driven self-supervised graph learning framework that directly optimizes the distortion-aware precoder under nonlinear correlated power amplifier (PA) distortion, without relying on expensive optimizer-generated labels for a user-centric satellite system. We minimize nonlinear PA consumption subject to user quality-of-service (QoS) constraints under PA nonlinearity for a downlink multi-input-single-output (MISO) satellite system. Our pipeline jointly designs the precoder and admission control that minimize nonlinear PA consumed power. In particular, a graph neural network (GNN) for the design of a distortion-aware precoder is optimized end-to-end in a self-supervised manner using a differentiable Itô-Hermite model of the true modified Rapp amplifier model. The precoder output by the GNN is used to initialize a low-complexity distortion-aware signal-to-interference-distortion (SINDR) tracking algorithm that jointly optimizes for power and admission control. Our proposed physics-based pipeline closely tracks a successive convex approximation (SCA) based solution in consumed power performance with the advantage of faster runtime and a wide operating range covered during training.

eess.SP↗

arXiv2026

Performance Analysis of Discrete-Activation Pinching-Antenna Systems

This paper investigates discrete-activation pinching antenna (DAPA) systems, where a finite set of pinching antennas (PAs) is deployed along a dielectric waveguide, from which PAs are selectively activated to serve users. Performance analyses are developed for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) by explicitly accounting for the system geometry and discrete PA activation. Specifically, analytical expressions for the outage probability (OP) and ergodic rate (ER) are derived. Furthermore, the discrete-to-continuous gain ratio (DCGR) is introduced to quantify the normalized ER gain of a finite PA deployment relative to the single-PA and continuous-activation references. Monte Carlo simulations validate the derived expressions and show that DAPA systems can closely approach continuous-activation performance with only a moderate number of candidate PAs. For example, under the considered setup, DAPA with five PAs recovers approximately 92% and 90% of the continuous-activation ER gain under OMA and NOMA, respectively, at a transmit power of 0 dBm.

eess.SP↗

arXiv2026

Distributed Constrained Resource Management in 6G Networks: A Scalable Hybrid Model-Learning Framework

Radio Resource Management (RRM) is a fundamental challenge in 6G wireless networks, particularly under dynamic user demands, inter-cell interference, and heterogeneous QoS constraints. Centralized optimization solutions are often infeasible in practice due to the lack of system-wide state information, dynamic conditions, and signaling delays, making distributed learning-based approaches attractive. However, conventional multi-agent reinforcement learning (MARL) struggles with scalability and constraint satisfaction in such highly dynamic environments. We introduce a scalable two-phase hybrid learning framework to address the RRM challenges where orthogonal-frequency division multiplexing (OFDM) domain knowledge is explicitly incorporated into the MARL pipeline. In our proposed two-phase learning framework, the first phase allocates a minimum resource to satisfy users' QoS requirements based on channel statistics, avoiding the inefficiencies of training policies under hard QoS constraints. Subsequently, a multiagent system is employed in the second phase to optimally allocate the remaining resources for system throughput maximization. By exploiting the structural interference information, we propose a scalable MARL algorithm which decomposes the original learning problem into smaller subproblems that can be handled independently, thereby avoiding exponential growth of the action space without compromising performance. Extensive simulations in realistic scenarios with 50MHz bandwidth and different numerologies show that our method significantly outperforms existing optimization and learning baselines, offering up to 40% throughput improvement, and 100% constraint satisfaction with minimal resource usage.

cs.IT↗

arXiv2026

Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity

The growing demand for on-device large language model (LLM) services on mobile edge devices has driven the adoption of Mixture-of-Experts (MoE) architectures, which scale model capacity with limited computation. Since fine-tuning MoE-based LLMs relies on privacy-sensitive local data, federated learning (FL) offers a natural paradigm for collaborative training without exposing raw data. However, integrating MoE-based LLM fine-tuning into FL faces two critical challenges caused by data heterogeneity across clients: (i) divergent local data distributions drive clients to develop distinct gating preferences, so direct parameter aggregation yields a one-size-fits-none global gating network; and (ii) same-indexed experts develop disparate semantic roles across devices, leading to expert semantic blurring and degraded specialization. To address these challenges, we propose FedAlign-MoE, a federated aggregation alignment framework for edge computing systems that jointly enforces routing consistency and expert semantic alignment. Specifically, FedAlign-MoE aggregates gating behaviors by aligning routing distributions through consistency weighting and optimizes local gating networks through distribution regularization, maintaining cross-client stability while preserving discriminative local gating preferences. Meanwhile, FedAlign-MoE quantifies the semantic consistency of same-indexed experts across devices and selectively aggregates semantically aligned experts, ensuring stable and specialized global experts. Extensive experiments demonstrate that FedAlign-MoE outperforms state-of-the-art benchmarks, achieving faster convergence and higher accuracy in non-IID federated environments with lightweight computation and efficient communication.

cs.LG↗

arXiv2026

Joint GKP Encoding for GHZ States Transmission over Bosonic Quantum MIMO Channels

We study transmission of a Greenberger-Horne-Zeilinger (GHZ) state over nonunitary quantum multiple-input multiple-output (QuMIMO) channels using finite-energy Gottesman-Kitaev-Preskill (GKP) encoding. A coupled-mode model describes coherent mixing, mode-dependent attenuation, and Gaussian noise. The transceiver combines programmable passive meshes, quantum-limited MMSE gain, local GKP recovery, and joint classical syndrome decoding. We optimize complex-Givens lattices using instantaneous channel-state information (CSI), current singular values with channel statistics, or statistics alone. Simulations show that joint lattice shaping outperforms product GKP processing. Under coherent-basis drift, full instantaneous CSI improves GHZ-state preservation, whereas singular-value conditioned and fully statistical designs perform similarly because neither resolves the current singular frames. These findings identify singular-frame geometry as a critical resource beyond attenuation knowledge where joint GKP processing adapts the logical lattice and receiver to anisotropic bosonic noise rather than merely inverting multimode mixing.

quant-ph↗

arXiv2026

One-Shot Beam Tracking for Stacked Intelligent Metasurfaces-Assisted LEO Broadcasting

This letter develops a physics-informed one-shot beam tracking method for stacked intelligent metasurface (SIM)-enabled low-Earth-orbit (LEO) links with $K$ users and an $L$-layer SIM with $N$ meta-atoms per layer. The predictable orbital motion is propagated through the line-of-sight (LoS) geometry to forecast the finite-interval variation of the user signal-to-noise ratio (SNR) profile, and an analytical multilayer Jacobian relates this variation to the $LN$ SIM phase coefficients. Beam tracking is formulated as normalized SNR-profile preservation and solved through a single regularized Gauss--Newton correction that requires one Jacobian evaluation and a $K\times K$ linear solve, thereby avoiding iterative reoptimization. A tracking-error bound separates the regularization residual from the nonlinear Taylor remainder and establishes a second-order local error under a full-row-rank Jacobian. Numerical results show that the proposed method retains approximately $94\%$ and $92\%$ of the iterative-reoptimization rate for $L=2$ and $L=4$, respectively, while running about $350\times$ and $260\times$ faster at $N=225$, and confirm the predicted second-order residual error behavior.

eess.SP↗

arXiv2026

Stacked Fluid Metasurfaces: Mutual-Coupling-Aware Modeling and Optimization

Stacked intelligent metasurfaces process the transmitted field layer by layer, and fluid antennas make the position of every radiator a design variable. Combined, they pack radiators at sub-wavelength spacings within and across layers, where mutual coupling governs the physics that current models omit. This paper develops a coupling-consistent model of a fluid transmit layer illuminating a stack of passive fluid beyond-diagonal layers. The multiport impedance matrix of any port constellation is shown to be the impedance kernel sampled at the three-dimensional separations, in one closed spherical-Hankel form whose in-plane restriction is intra-layer coupling and whose axial restriction replaces the scalar Rayleigh--Sommerfeld propagator. Only the transmitter is driven: the layer currents are induced, and the familiar cascade is the single-pass limit of one matrix inversion. In the wavenumber domain the light circle still separates radiation from reaction, now with a plane-wave propagator attached, every Fourier mode sees a transmission line loaded by the layers, and an efficiency identity shows that what the stack costs in efficiency is set by its total induced-current norm, not by its layer count. Because the load is never assumed layer-diagonal, interconnections may join atoms on different sheets, and vertical networks repeated at every site are spectrally local. Alternating optimization designs precoders, positions, and loads with closed-form gradients. Numerically, fluid ports on a $λ/8$ grid perform within a few per cent of continuously movable ones, while a design based on the conventional cascade model can do worse than open-circuiting the stack.

eess.SP↗

arXiv2026

LIMODENet: Attention-Free Compact Encoders for Information-Preserving Onboard Satellite Image Restoration

Onboard satellites must restore a channel-degraded image on a few watts, using neuromorphic accelerators (e.g., BrainChip Akida, Intel Loihi-2) that support no softmax or attention. We ask which encoder restores best under that constraint and introduce LIMODENet (LinearMix-ODENet), a 0.69M softmax-/QKV-free backbone whose residual stages read as ODE discretizations and which is empirically information-preserving (probe accuracy rises 79.9% -> 98.4% from stem to head). At iso-parameters it restores 1 dB DVB-S2X-degraded EuroSAT better than a CNN autoencoder (+1.75 dB PSNR) and a skip-connection U-Net (+1.07 dB), three seeds, non-overlapping. Unconstrained modern restorers (NAFNet, Restormer) win on fidelity; we decompose that gap: spiking-legal additive skips recover about half, and the rest traces to attention and channel gating. LIMODENet then converts end-to-end to a spiking network with zero blocked operations, versus 22-24 for the competitors: not the best restorer available, but the best verified deployable within a real power budget.

cs.CV↗

arXiv2026

GNN-Based Polarforming for Multi-User MISO Short-Packet URLLC under Imperfect CSI

This paper investigates polarization-aware transmission for multi-user multiple-input single-output (MU-MISO) short-packet ultra-reliable low-latency communications (URLLC) under imperfect channel state information (CSI). We consider a system in which the base station (BS) and users are equipped with polarization-reconfigurable antennas that enable adaptive polarization states through controllable polarization coefficients. A multi-objective optimization problem is formulated to jointly maximize the finite-blocklength (FBL) achievable sum rate and minimize the maximum decoding error probability (DEP), subject to transmit-power, latency, reliability, and discrete polarization-control constraints. The resulting multi-objective problem is scalarized using a normalized weighted-sum utility. To enable low-complexity online decision-making, a heterogeneous graph neural network (GNN) is developed to learn the joint mapping from estimated polarized CSI to digital beamforming and transmit/receive polarforming vectors (PFVs) while accounting for the system constraints. Numerical results demonstrate that the proposed GNN-based polarforming (PF) framework substantially improves the FBL sum rate while reducing the maximum DEP compared with conventional fixed-polarization schemes, particularly under channel depolarization and imperfect CSI. This highlights the potential of adaptive polarization control for reliable low-latency transmission.

cs.IT↗

arXiv2026

Polarforming-Enabled Power-Splitting SWIPT: A GNN-Based Optimization Approach

Simultaneous wireless information and power transfer (SWIPT) is a critical technology for the future of the Internet of Things (IoT). However, ensuring a stable power supply in such networks remains a significant challenge. This work introduces dynamic polarization control as an additional degree of freedom (DoF) in SWIPT systems. We propose a system where both the base station (BS) and the users can adjust their antenna polarization, a technique known as polarforming. In addition, each user device is capable of splitting the incident signal to perform simultaneous information decoding (ID) and energy harvesting (EH). The resulting non-convex optimization, with many coupled variables, is solved using a graph neural network (GNN) that learns the sub-optimal beamforming, polarization, and power-splitting variables. Simulation results demonstrate that the proposed GNN-based dynamic polarforming optimization significantly outperforms fixed-polarization schemes, particularly under imperfect channel state information (CSI). Moreover, joint polarforming and GNN-based optimization maintain robust SWIPT performance under both polarization mismatch and imperfect CSI.

cs.IT↗