arXiv subjects

Mazen Hasna

Publications and source records attributed to Mazen Hasna.

At least 19 recordsLinked to original sources

arXiv2026

OHL-Assisted All-Optical Regenerative Relaying for Pointing-Impaired M-PAM Inter-Satellite Links

Rapid inter-satellite traffic growth in LEO constellations demands spectrally efficient, low-latency optical relaying. While amplify and forward (AF) relays are latency-efficient, they propagate noise; conversely, decode and forward (DF) relays suppress noise but incur significant complexity via O/E/O conversion. This paper proposes an all-optical regenerative relay for M-ary pulse amplitude modulation (M-PAM) multi-hop links under pointing errors. A parallel optical hard-limiter (OHL) bank performs symbol-level discrimination, regenerating signal levels directly in the optical domain. A variable gain EDFA is employed to stabilize power and define the threshold-stable region. By incorporating pointing-induced fading, various noise sources including ASE-ASE and signal-ASE beat noise and implementation-dependent decision noise, we derive closed-form per-hop symbol error rate (SER) expressions. These are extended to end-to-end performance using a Markov transition-matrix model for arbitrary modulation order and hop count. Analysis of beamwidth, pointing accuracy, and threshold scaling demonstrates reliable multi-hop operation, avoiding both AF noise accumulation and DF O/E/O processing overhead. Verified by Monte Carlo simulations and numerical integration, this framework provides a design benchmark for low-latency, pointing-aware all-optical regenerative relaying.

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arXiv2025

When Future Communications Shift Toward Narrow Beams: A Forward Looking Survey on Pointing Errors and Alignment Limits

Directional links in free-space optical (FSO), millimeter-wave (mmWave), and terahertz (THz) systems are a cornerstone of emerging 6G networks, yet their reliability is fundamentally limited by pointing errors and misalignment. Existing studies address this impairment using technology-specific definitions, models, and mitigation approaches, which hinders cross-domain comparison and transferable design insight. This survey provides a unified treatment of pointing errors across optical and high frequency wireless communications. We establish consistent terminology and a cross-technology taxonomy of pointing errors, review angular misalignment and statistical distribution models, and analyze their impact on system performance. Mitigation techniques are systematically surveyed with emphasis on optical systems and their connection to underlying pointing error models. The survey further provides a detailed examination of pointing-error effects in orbital angular momentum (OAM) links and quantum optical communications, and surveys the corresponding mitigation approaches tailored to mode-dependent impairments and quantum measurement constraints. The survey also outlines open challenges and future research directions. By consolidating fragmented literature into a coherent framework, this work supports consistent analysis and robust design of next generation directional communication systems.

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arXiv2025

Balancing Decentralized Trust and Physical Evidence: A Blockchain-Physical Layer Co-Design for Real-Time 3D Prioritization in Disaster Zones

During disaster response, making rapid and well-informed decisions about which areas require immediate attention can save lives. However, current coordination models often struggle with unreliable data, intentional misinformation, and the breakdown of critical communication infrastructure. A decentralized, vote-based blockchain model offers a compelling substrate for achieving this real-time, trusted coordination. This article explores a blockchain-driven approach to rapidly update a dynamic 3D crisis map based on inputs from users and local sensors. Each node submits a timestamped and geotagged vote to a public ledger, enabling agencies to visualize needs as they emerge. However, ensuring the physical authenticity of these claims demands more than cryptography alone. We propose a dual-layer architecture where mobile UAV verifiers perform physical-layer attestation and issue independent location flags to the blockchain. This dual-signature mechanism fuses immutable digital records with sensory-grounded trust. We analyze core technical and human centric challenges, ranging from spoofing and vote ambiguity to verifier compromise and connectivity loss, and outline layered mitigation strategies and future research directions. As a concrete instantiation, we present a UAV mapping scheme leveraging modulated retro-reflector (MRR) sensors and 3D-aware LoS placement to maximize verifiability under urban occlusion, offering a path toward resilient, trust-anchored crisis coordination.

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arXiv2025

Towards City-Scale Quantum Timing: Wireless Synchronization via Quantum Hubs

This paper presents a novel wireless quantum synchronization framework tailored for city-scale deployment using entangled photon pairs and passive corner cube retroreflector (CCR) arrays. A centralized quantum hub emits entangled photons, directing one toward a target device and the other toward a local reference unit. The target, equipped with a planar CCR array, reflects the incoming photon without active circuitry, enabling secure round-trip quantum measurements for sub-nanosecond synchronization and localization. We develop a comprehensive analytical model that captures key physical-layer phenomena, including Gaussian beam spread, spatial misalignment, atmospheric turbulence, and probabilistic photon generation. A closed-form expression is derived for the single-photon detection probability under Gamma-Gamma fading, and its distribution is used to model photon arrival events and synchronization error. Moreover, we analyze the impact of background photons, SPAD detector jitter, and quantum generation randomness on synchronization accuracy and outage probability. Simulation results confirm the accuracy of the analytical models and reveal key trade-offs among beam waist, CCR array size, and background light. The proposed architecture offers a low-power, infrastructure-free solution for secure timing in next-generation smart cities.

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arXiv2025

QoS- and Physics-Aware Routing in Optical LEO Satellite Networks via Deep Reinforcement Learning

Optical inter-satellite links (ISLs) are becoming the principal communication backbone in modern large-scale LEO constellations, offering multi-Gb/s capacity and near speed-of-light latency. However, the extreme sensitivity of optical beams to relative satellite motion, pointing jitter, and rapidly evolving geometry makes routing fundamentally more challenging than in RF-based systems. In particular, intra-plane and inter-plane ISLs exhibit markedly different stability and feasible range profiles, producing a dynamic, partially constrained connectivity structure that must be respected by any physically consistent routing strategy. This paper presents a lightweight geometry- and QoS-aware routing framework for optical LEO networks that incorporates class-dependent feasibility constraints derived from a jitter-aware Gaussian-beam model. These analytically computed thresholds are embedded directly into the time-varying ISL graph and enforced via feasible-action masking in a deep reinforcement learning (DRL) agent. The proposed method leverages local geometric progress, feasible-neighbor structure, and congestion indicators to select next-hop relays without requiring global recomputation. Simulation results on a Starlink-like constellation show that the learned paths are physically consistent, exploit intra-plane stability, adapt to jitter-limited inter-plane connectivity, and maintain robust end-to-end latency under dynamic topology evolution.

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arXiv2025

Deep Learning Surrogate for Fast CIR Prediction in Reactive Molecular Diffusion Advection Channels

Accurate channel impulse response (CIR) modeling in molecular communication (MC) often requires solving coupled reactive diffusion-advection equations, which is computationally expensive for large parameter sweeps or design loops. We develop a deep-learning surrogate for a three-dimensional duct MC channel with reactive diffusion-advection transport and reversible ligand-receptor binding on a finite ring receiver. Using a physics-based partial differential equation (PDE)-ordinary differential equation (ODE) model, we generate a large CIR dataset across broad transport, reaction, and geometric ranges and train a neural network that maps these parameters directly to the CIR. On an independent test set, the surrogate closely matches reference CIRs both qualitatively and quantitatively: the empirical cumulative distribution function (CDF) of the normalized root mean square error (NRMSE) shows that 90% of test channels are predicted with error below 0.15, with only weak dependence on individual parameters. The surrogate therefore offers an accurate and computationally efficient replacement for repeated PDE-based CIR evaluations in MC system analysis and design.

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arXiv2025

AI Assisted Next Gen Outdoor Optical Networks: Camera Sensing for Monitoring and User Localization

We consider outdoor optical access points (OAPs), which, enabled by recent advances in metasurface technology, have attracted growing interest. While OAPs promise high data rates and strong physical-layer security, practical deployments still expose vulnerabilities and misuse patterns that necessitate a dedicated monitoring layer - the focus of this work. We therefore propose a user positioning and monitoring system that infers locations from spatial intensity measurements on a photodetector (PD) array. Specifically, our hybrid approach couples an optics-informed forward model and sparse, model-based inversion with a lightweight data-driven calibration stage, yielding high accuracy at low computational cost. This design preserves the interpretability and stability of model-based reconstruction while leveraging learning to absorb residual nonidealities and device-specific distortions. Under identical hardware and training conditions (both with 5 x 10^5 samples), the hybrid method attains consistently lower mean-squared error than a generic deep-learning baseline while using substantially less training time and compute. Accuracy improves with array resolution and saturates around 60 x 60-80 x 80, indicating a favorable accuracy-complexity trade-off for real-time deployment. The resulting position estimates can be cross-checked with real-time network logs to enable continuous monitoring, anomaly detection (e.g., potential eavesdropping), and access control in outdoor optical access networks.

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arXiv2025

CV Quantum Communications with Angular Rejection Filtering: Modeling and Security Analysis

Continuous-variable quantum key distribution (CVQKD) over free-space optical links is a promising approach for secure communication, but its performance is limited by turbulence, pointing errors, and angular leakage that can be exploited by an eavesdropper. To mitigate this, we consider an angular rejection filter that defines a safe-zone at the receiver and blocks signals from outside the desired cone. A system and channel model is developed including turbulence, misalignment, and safe-zone effects, and information theoretic metrics are derived to evaluate security. Simulation results show that the safe zone significantly reduces information leakage and that careful tuning of beam waist, angular threshold, and aperture size is essential for maximizing the secret key rate. Larger apertures improve performance but increase receiver size, while longer links require sub 100 urad alignment accuracy. These results highlight safe-zone enforcement and parameter optimization as effective strategies for practical and secure CV-QKD.

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arXiv2025

Hierarchical Deep Learning for Joint Turbulence and PE Estimation in Multi-Aperture FSO Systems

Accurate characterization of free-space optical (FSO) channels requires joint estimation of transmitter pointing errors, receiver angle-of-arrival (AoA) fluctuations, and turbulence-induced fading. However, existing literature addresses these impairments in isolation, since their multiplicative coupling in the received signal severely limits conventional estimators and prevents simultaneous recovery. In this paper, we introduce a novel multi-aperture FSO receiver architecture that leverages spatial diversity across a lens array to decouple these intertwined effects. Building on this hardware design, we propose a hierarchical deep learning framework that sequentially estimates AoA, transmitter pointing error, and turbulence coefficients. This decomposition significantly reduces learning complexity and enables robust inference even under strong atmospheric fading. Simulation results demonstrate that the proposed method achieves near-MAP accuracy with orders-of-magnitude lower computational cost, and substantially outperforms end-to-end learning baselines in terms of estimation accuracy and generalization. To the best of our knowledge, this is the first work to demonstrate practical joint estimation of these three key parameters, paving the way for reliable, turbulence-resilient multi-aperture FSO systems.

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arXiv2025

Learning-Driven Dual-Line Laser Scanning for Fast and Accurate LEO Satellite Positioning

Accurate and low-latency positioning is a key enabler for optical links with Low Earth Orbit (LEO) satellites, where millisecond-level beam alignment is required to maintain reliable high-data-rate communication. This paper presents a learning-driven dual-line laser scanning framework for fast and precise satellite positioning. Unlike conventional Gaussian-beam acquisition systems that rely on multiple sequential beams or mechanical steering, the proposed approach employs two orthogonal line-shaped laser beams to perform structured optical scanning over the ambiguity region without any moving parts. A physics-based model incorporating atmospheric attenuation, turbulence, and MRR-based reflection is developed, and a data-driven neural estimator is trained to map received optical energy patterns to the satellite's two-dimensional position. Simulation results demonstrate that the learning-driven method achieves near-MAP accuracy with typical errors of 7-10 m and deterministic scanning time of 1-2 ms, while conventional two-stage Gaussian-beam schemes exhibit comparable errors but random sensing durations of up to 5 ms. The proposed framework therefore offers a favorable trade-off between positioning accuracy, computational complexity, and sensing latency, making it a practical candidate for next-generation optical LEO tracking systems.

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arXiv2025

MRR-Based Line-Laser Scanning for Reliable Vehicular Positioning and Optical Communication

High-speed vehicular environments require optical systems capable of joint sensing, positioning, and communication (JSPC) without mechanical tracking. Existing optical and integrated sensing-communication approaches often rely on point-source emitters or camera-based receivers, limiting spatial coverage and update rate under highway dynamics. This work introduces a new class of tracking-free optical JSPC systems that combine structured line-laser illumination with modulating retroreflector (MRR) arrays on vehicles. Two orthogonal line lasers perform synchronized longitudinal and transverse scanning to provide continuous, wide-area coverage across the roadway. A coverage-driven analytical framework models the coupling between beam divergence, scan geometry, and dwell-time allocation, enabling joint evaluation of sensing reliability and communication quality. An optimization scheme is developed to adapt scanning and divergence parameters for uniform coverage and power efficiency. Simulation results demonstrate significant improvements in spatial coverage uniformity, link stability, and reliability within a fixed scan period. These results establish a practical pathway toward scalable, turbulence-resilient optical architectures for next-generation vehicular JSPC networks.

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arXiv2025

Picosecond Wireless Synchronization with Entangled Photons via Grid-Based Quantum Coverage in Indoor Optical Systems

In this paper, we present a robust entanglement-assisted synchronization framework for indoor optical wireless systems that explicitly captures the coupling between spatial beam geometry and temporal synchronization accuracy. Unlike conventional approaches that treat beam steering and timing estimation independently, a unified spatio temporal model is developed that links user position uncertainty to the Cramer Rao lower bound of the synchronization error. The framework incorporates key physical impairments, including multipath dispersion, non Gaussian detector jitter, and spatially correlated localization errors. Through analytical modeling and extensive simulations, we show that the proposed system exhibits graceful performance degradation under heavy tailed positioning uncertainty and remains stable in the presence of multipath induced bias. Using realistic single photon detector parameters, the results indicate that synchronization accuracy below $10$ picoseconds can be maintained across a wide range of operating conditions. This level of precision provides a scalable foundation for quantum enabled indoor wireless networks.

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arXiv2025

Robust Belief-State Policy Learning for Quantum Network Routing Under Decoherence and Time-Varying Conditions

Quantum network routing requires online decisions under probabilistic entanglement generation, finite quantum memories, decoherence, imperfect operations, and classical feedback, while the controller has incomplete knowledge of the physical state. This paper develops a robust belief-state routing framework based on a quantum partially observable Markov decision process (q-POMDP) and a feasibility-masked graph neural network (GNN). The model uses atomic micro-epochs in which each selected operation completes before the next decision boundary. This enables explicit accounting of memory reservations, pair-instance inventories, purification consumption, swapping outcomes, release decisions, queue service, and completion-time delivery fidelity. The controller maintains a classical belief over hidden physical states, including latent environmental conditions, and uses this belief to evaluate feasible actions and update posterior pair states. To make planning scalable, we introduce feasibility-stratified prototypes, identifier-free signatures, and role-aware action matching, which preserve hard resource constraints while enabling value transfer across structurally similar information states. A cached q-POMDP planner is then fused with a role-aware GNN policy through an adaptive trust rule, with a safe fallback for previously unseen feasibility signatures. We provide theoretical guarantees on feasibility, value approximation, policy performance, robustness, regret, and learning variance. Simulations over finite-memory quantum-network topologies show that the proposed hybrid controller improves high-fidelity goodput, reduces below-threshold deliveries, and maintains lower online decision cost than planner-only control, while outperforming heuristic, purification-aware, and learning-based baselines.

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arXiv2025

All-Optical Inter-Satellite Relays with Intelligent Beam Control: Harnessing Liquid Lenses and Optical Hard Limiters

Low Earth orbit (LEO) satellite constellations are emerging as a key enabler of next-generation communications, offering global coverage and significantly lower latency compared to traditional terrestrial networks and geostationary satellites. However, further latency reduction is essential for time-critical applications such as real-time sensing, autonomous systems, and interactive services. One critical bottleneck is the optical-to-electrical (O/E) and electrical-to-optical (E/O) conversions at intermediate nodes in multi-hop links, which introduce unwanted processing delays. To address this, we investigate an all-optical relay system based on Optical Hard Limiters (OHL), which operate purely in the optical domain to suppress noise and restore signal quality without requiring O/E conversions. First, we present a rigorous analysis of inter-satellite multi-relay communication under the OHL relaying architecture, comparing it against conventional Amplify-and-Forward (AF) and Decode-and-Forward (DF) schemes. Through this comparison, we highlight both the advantages and limitations of OHL relays, including their particular sensitivity to parameter choices such as the threshold setting and divergence angle at the transmitter. Recognizing that a LEO constellation is inherently time-varying - satellites move relative to one another, causing continuous changes in link distances and tracking errors - we propose a joint optimization strategy. This scheme adaptively tunes the OHL decision threshold and beam divergence in real time to maintain optimal performance, ultimately lowering error rates and latency. Extensive simulations in a large-scale LEO network demonstrate the viability of our method and offer insights into practical implementation for next-generation inter-satellite communication systems.

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arXiv2025

A Unified Framework for UAV-Based Free-Space Quantum Links: Beam Shaping and Adaptive Field-of-View Control

This paper develops a comprehensive analytical framework for modeling and performance evaluation of unmanned aerial vehicles (UAVs)-to-ground quantum communication links, incorporating key physical impairments such as beam divergence, pointing errors at both transmitter and receiver, atmospheric attenuation, turbulence-induced fading, narrow field-of-view (FoV) filtering, and background photon noise. To overcome the limitations of conventional wide-beam assumptions, we introduce a grid-based approximation for photon capture probability that remains accurate under tightly focused beams. Analytical expressions are derived for the quantum key generation rate and quantum bit error rate (QBER), enabling fast and reliable system-level evaluation. Our results reveal that secure quantum key distribution (QKD) over UAV-based free-space optical (FSO) links requires beam waists below 10 cm and sub-milliradian tracking precision to achieve Mbps-level key rates and QBER below $10^{-3}$. Additionally, we highlight the critical role of receiver FoV in balancing background noise rejection and misalignment tolerance, and propose adaptive FoV tuning strategies under varying illumination and alignment conditions. The proposed framework provides a tractable and accurate tool for the design, optimization, and deployment of next-generation airborne quantum communication systems.

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arXiv2025

Physical Limits of Entanglement-Based Quantum Key Distribution over Long-Distance Satellite Links

Entanglement-based quantum key distribution (QKD) protocols, such as E91 and BBM92, offer strong information-theoretic security and are naturally suited for satellite-to-satellite QKD (SatQKD) links. However, implementing these protocols over long-distance inter-satellite free-space optical (FSO) channels poses critical physical-layer challenges that are not addressed in the existing literature. In particular, photon losses due to beam divergence, pointing errors, and background noise can severely degrade the key generation rate and quantum bit error rate (QBER), especially under narrow receiver field-of-view (FoV) constraints. This paper presents a comprehensive performance analysis of entanglement-based inter-satellite QKD, focusing on photon-level modeling and the impact of practical impairments. We develop analytical expressions for signal detection probabilities, background photon influence, multi-pair emissions, and QBER, incorporating key parameters such as link distance, transmitter tracking jitter, receiver misalignment, and photon pair generation rate. Simulation results reveal the nonlinear sensitivity of system performance to tracking error and FoV limitations, and highlight optimal parameter regimes that jointly maximize secret key rate while maintaining QBER below acceptable thresholds. The proposed model provides actionable design insights for reliable and efficient deployment of entanglement-based SatQKD systems.

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arXiv2025

Compact Analytical Model for Real-Time Evaluation of OAM-Based Inter-Satellite Links

This paper presents an efficient analytical framework for evaluating the performance of inter-satellite communication systems utilizing orbital angular momentum (OAM) beams under pointing errors. An accurate analytical model is first developed to characterize intermodal crosstalk caused by beam misalignment in OAM-based inter-satellite links. Building upon this model, we derive efficient expressions to analyze and optimize system performance in terms of bit error rate (BER). Unlike traditional Monte Carlo-based methods that are computationally intensive, the proposed approach offers accurate performance predictions. This enables a substantial decrease in computation time while maintaining high accuracy, thanks to the use of analytical expressions for both crosstalk and BER. This fast and accurate evaluation capability is particularly critical for dynamic low Earth orbit (LEO) satellite constellations, where network topology and channel conditions change rapidly, requiring real-time link adaptation. Furthermore, we systematically design and evaluate asymmetric OAM mode sets, which significantly outperform symmetric configurations in the presence of pointing errors. Our results also reveal key insights into the interaction between beam divergence, tracking accuracy, and link distance, demonstrating that the proposed framework enables real-time optimization of system parameters with high fidelity. The analytical findings are rigorously validated against extensive Monte Carlo simulations, confirming their practical applicability for high-mobility optical wireless systems such as LEO satellite networks.

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arXiv2024

Optimizing UAV Trajectory for Emergency Response Operations under Real 3D Environments: Integrating Priority Levels and LoS Constraints

Unmanned Aerial Vehicles (UAVs) have emerged as a critical component in next-generation wireless networks, particularly for disaster recovery scenarios, due to their flexibility, mobility, and rapid deployment capabilities. This paper focuses on optimizing UAV trajectories to ensure effective communication in disaster-stricken areas using terahertz (THz) links. We address specific challenges such as energy consumption, user priority levels, and navigating complex urban environments to maintain Line of Sight (LoS) connections amidst 3D obstacles. Our contributions include the development of a detailed modeling approach using online 3D map data, the formulation of an optimal trajectory optimization problem, and the proposal of a Genetic Algorithm (GA)-based method alongside an enhanced heuristic algorithm for faster convergence. Through 3D simulations, we demonstrate the trade-off between minimizing total service time and prioritizing higher-weight nodes, showing the impact of different priority weight factors on the trajectory time. The proposed algorithms are evaluated using real-world data from the West Bay area of Doha, Qatar, demonstrating their effectiveness in optimizing UAV trajectories for emergency response.

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