arXiv subjects

Khalid Qaraqe

Publications and source records attributed to Khalid Qaraqe.

At least 19 recordsLinked to original sources

arXiv2026

Learning-Free Hierarchical Joint Estimation of AoA, Pointing Error, Receiver Jitter, and Turbulence in Multi-Aperture FSO Systems

This paper proposes a learning free hierarchical estimator for jointly recovering the angle of arrival (AoA), transmitter pointing error, receiver induced jitter, and per aperture turbulence coefficients in a multi aperture free space optical system. The proposed method exploits the distinct spatial signatures contained in quad photodetector measurements. First, normalized quadrant imbalance ratios provide an approximate closed form AoA estimate. Next, AoA compensated lens powers are transformed into a log linear regression model for non iterative pointing error estimation. Finally, receiver jitter and turbulence coefficients are directly reconstructed after compensating for the estimated angular losses. The method requires neither neural network training nor exhaustive multidimensional search and has computational complexity linear in the number of lenses. Monte Carlo results demonstrate robust AoA estimation under Gamma Gamma turbulence, reveal turbulence induced error floors in OLS based pointing and jitter estimation, and show improved turbulence reconstruction with larger arrays and explicit pointing compensation.

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arXiv2026

Quantifying the Reality Gap for RL-Based UAV Placement at mmWave and Sub-THz

Reinforcement learning (RL) policies for unmanned aerial vehicle (UAV) placement in mmWave and sub-terahertz networks are typically trained on simplified analytical channels. We quantify the resulting sim-to-real gap on a real urban map of Doha, Qatar, at carriers {28, 140, 183, 300} GHz and altitudes {50, 75, 100, 125} m, evaluating three channel pipelines: an analytical model (FSPL + atmospheric absorption + cuboid LoS), full Monte-Carlo ray tracing in Sionna RT with ITU-R P.676-13 absorption, and a deterministic-LoS hybrid that reuses Sionna's mesh under a closed-form path-gain expression. We formalize the gap on the spatial SNR distribution via four metrics, namely bias, RMSE, Jensen-Shannon divergence, and optimum-deployment displacement. Three findings emerge: at 28/140 GHz, $\sim$70% of the apparent -5.6/-4.8 dB Sionna bias is Monte-Carlo undersampling and shrinks to -1.7/-1.5 dB after mitigation; at 183 GHz a -9.2 dB residual isolates the atmospheric absorption / ITU-R P.676 line-shape disagreement; at 300 GHz the stochastic ray tracer agrees with the analytical model only coincidentally, with a +3.8 dB structural offset exposed by the deterministic-LoS pipeline. Across all carriers the linear-domain regret of the analytical-trained policy stays $\geq$ 0.93, indicating practical near-optimality but with a carrier-resolved SNR bias that warrants explicit reporting.

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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

Audit-of-Understanding: Posterior-Constrained Inference for Mathematical Reasoning in Language Models

Large language models (LLMs) often generate reasoning traces that appear coherent but rest on unsupported assumptions, leading to hallucinated conclusions. Prior work mainly addresses factual hallucinations or relies on post-hoc verification, leaving reasoning-induced hallucinations largely unaddressed. We propose Audit-of-Understanding (AoU), a framework that constrains inference to validated premises through three phases: (1) decomposing a query into candidate assumptions, (2) auditing their support, and (3) conditioning inference only on the validated subset. Formally, AoU is \emph{posterior-constrained inference}, connecting to selective prediction and rejection learning. Our contributions are threefold: (i) theoretical guarantees under perfect validation, (ii) excess-risk bounds under imperfect audits, and (iii) tractability analysis. Empirically, AoU improves both accuracy and faithfulness on GSM8K, MultiArith, and SVAMP, achieving up to +30% gains on GSM8K, +45% on MultiArith, and consistent +20--28% improvements on SVAMP over Chain-of-Thought, Self-Consistency, and CoT-Decoding. Code is available at https://anonymous.4open.science/r/audit-of-understanding-E28B.

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arXiv2025

Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference

Large language models (LLMs) are increasingly applied in multilingual contexts, yet their capacity for consistent, logically grounded alignment across languages remains underexplored. We present a controlled evaluation framework for multilingual natural language inference (NLI) that generates synthetic, logic-based premise-hypothesis pairs and translates them into a typologically diverse set of languages. This design enables precise control over semantic relations and allows testing in both monolingual and mixed-language (code-switched) conditions. Surprisingly, code-switching does not degrade, and can even improve, performance, suggesting that translation-induced lexical variation may serve as a regularization signal. We validate semantic preservation through embedding-based similarity analyses and cross-lingual alignment visualizations, confirming the fidelity of translated pairs. Our findings expose both the potential and the brittleness of current LLM cross-lingual reasoning, and identify code-switching as a promising lever for improving multilingual robustness. Code available at: https://github.com/KurbanIntelligenceLab/nli-stress-testing

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arXiv2025

Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models

Large language models (LLMs) have shown strong performance across natural language reasoning tasks, yet their reasoning processes remain brittle and difficult to interpret. Prompting techniques like Chain-of-Thought (CoT) enhance reliability by eliciting intermediate reasoning steps or aggregating multiple outputs. However, they lack mechanisms for enforcing logical structure and assessing internal coherence. We introduce Theorem-of-Thought (ToTh), a novel framework that models reasoning as collaboration among three parallel agents, each simulating a distinct mode of inference: abductive, deductive, and inductive. Each agent produces a reasoning trace, which is structured into a formal reasoning graph. To evaluate consistency, we apply Bayesian belief propagation guided by natural language inference (NLI), assigning confidence scores to each step. The most coherent graph is selected to derive the final answer. Experiments on symbolic (WebOfLies) and numerical (MultiArith) reasoning benchmarks show that ToTh consistently outperforms CoT, Self-Consistency, and CoT-Decoding across multiple LLMs, while producing interpretable and logically grounded reasoning chains. Our findings suggest a promising direction for building more robust and cognitively inspired LLM reasoning. The implementation is available at https://github.com/KurbanIntelligenceLab/theorem-of-thought.

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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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