arXiv subjects

Tareq Y. Al-Naffouri

Publications and source records attributed to Tareq Y. Al-Naffouri.

At least 19 recordsLinked to original sources

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.

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arXiv2026

LEO Constellations as a Decentralized GNSS Network: Optimizing PNT Corrections in Space

With the rapid expansion of low Earth orbit (LEO) constellations, thousands of satellites are now in operation, many equipped with onboard GNSS receivers capable of continuous orbit determination and time synchronization. This development is creating an unprecedented spaceborne GNSS network, offering new opportunities for network-driven precise LEO orbit and clock estimation. Yet, current onboard GNSS processing is largely standalone and often insufficient for high-precision applications, while centralized fusion is challenging due to computational bottlenecks and the lack of in-orbit infrastructure. In this work, we report a decentralized GNSS network over large-scale LEO constellations, where each satellite processes its own measurements while exchanging compact information with neighboring nodes to enable precise orbit and time determination. We model the moving constellation as a dynamic graph and employ a momentum-accelerated gradient tracking (GT) method to achieve stable convergence despite topology changes. Numerical simulations with constellations containing hundreds of satellites show that the proposed method matches the accuracy of an ideal centralized benchmark. Ultimately, this framework supports the development of autonomous and self-organizing space systems, enabling high-precision navigation with reduced dependence on continuous ground contact.

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arXiv2026

Beamforming Design for Stem-Connected Microwave Linear Analog Computer (MiLAC)-Aided Multiuser MISO Downlinks

A microwave linear analog computer (MiLAC) is a tunable microwave network that leverages wave propagation to perform computation directly in the analog domain. MiLAC finds promising applications in beamforming, where it can map the data streams to the antennas without a digital beamformer, using one radio-frequency chain per stream. A fully-connected MiLAC needs a number of tunable components that grows quadratically with the number of antennas, whereas a stem-connected MiLAC needs only a linearly growing number and still achieves the point-to-point capacity. For the multiuser multiple-input single-output downlink, we prove that the stem-connected MiLAC attains the maximum sum-rate of the fully-connected MiLAC whenever $N\ge 2K-1$, where $N$ and $K$ denote the numbers of antennas and users. The proof shows that restricting the design to beamformers with orthonormal columns, which the stem-connected MiLAC realizes in closed form, loses no sum-rate. Based on this restriction, we develop, for every $N\ge K$, a weighted minimum mean-square error algorithm with a Riemannian beamformer update and prove that its accumulation points are stationary. For the bounded, discrete susceptances of practical components, we then propose an alternating refinement that adjusts one susceptance at a time through rank-one updates and never decreases the sum-rate. With $64$ antennas, four users, and ideal components, the stem-connected MiLAC attains the fully-connected sum-rate with $4.5$ times fewer tunable components. With $3$-bit susceptances at a signal-to-noise ratio of $10$~dB, the refinement retains $90\%$ of this sum-rate.

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arXiv2026

Pre- and Post-Exercise Monitoring of Physiological Skin States in Sportsmen Using 77 GHz FMCW Radar and Wavelet Scattering Transform

This paper presents a contactless radar sensing framework that classifies physiologically-induced changes in the superficial skin layer following intense fixed-duration physical activity along with water intake restriction. A 77 GHz frequency-modulated continuous wave (FMCW) radar is used to capture high-resolution range profiles from the subject's chest, seated in front of the radar, before and after sports activity. Since the electromagnetic penetration into biological tissue is inherently shallow at millimeter-wave frequencies, the sensing is confined primarily to the stratum corneum and upper epidermis. We demonstrate that exercise-induced thermoregulation, perspiration residue deposition, and transient shifts in superficial tissue hydration produce measurable alterations in quasi-static radar reflectivity. We utilize single-chirp snapshots of duration 102.4 micro sec in order to suppress confounders such as slow-varying cardiopulmonary motion, followed by lightweight feature extraction via the Wavelet Scattering Transform (WST) and classification using light-weight machine learning models suitable for edge nodes. Leave-one-subject-out cross-validation on a dataset of 15 sportsmen measured pre- and post-exercise yields a mean classification accuracy of 91.55% with a 95% confidence interval of [84.27, 98.83]%. Interpretability analysis reveals that low-order scattering coefficients (capturing overall signal energy and amplitude stability) dominate discriminative power, consistent with quasi-static changes in skin surface permittivity. The results establish a proof-of-concept for radar-based, edge deployable classification of post-exercise skin states, opening new avenues for non-contact physiological monitoring in sports science.

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arXiv2026

Quantization-Aware EE Optimization and SE-EE Tradeoff for MiLAC-Aided MU-MISO Beamforming

In large antenna arrays, hardware power consumption becomes a dominant design constraint, making energy efficiency (EE) a first-class objective alongside spectral efficiency (SE). Microwave linear analog computer (MiLAC)-aided beamforming, whose front end is a passive reciprocal stream-to-antenna network, addresses this tension by reducing the active radio-frequency chain count to the stream number, at a moderate SE cost. Despite this promise, no EE optimization framework has been established for MiLAC-aided beamforming that accounts for digital-to-analog converter quantization noise and post-quantized transmit power. We fill this gap for downlink multiuser multiple-input single-output (MU-MISO) systems by formulating quantization-aware EE maximization over the MiLAC-feasible beamformer and characterizing the resulting SE-EE tradeoff. Three contributions follow. First, we prove a row-space optimality property of the effective MiLAC-aided beamformer, yielding an equivalent reduced-dimension reformulation whose complexity scales with the stream number rather than the antenna number. Second, we develop a low-complexity Dinkelbach-weighted minimum mean-square error algorithm aided by projected gradient descent that is guaranteed to converge to a stationary point. Third, we cast the SE-EE tradeoff as a multi-objective problem and trace its Pareto boundary via a weighted-sum method that combines an alternative reduced-dimension coordinate with auxiliary-variable successive convex approximation, yielding convex per-iteration subproblems with guaranteed convergence. Numerical results on a DeepMIMO v4 deployment show MiLAC-aided beamforming substantially improves EE over digital and hybrid benchmarks at a moderate SE cost and significantly expands the achievable SE-EE operating region.

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arXiv2026

Learning-based near- versus far-field boundaries for ultra-massive MIMO communications

Signal processing techniques for wireless communications and sensing fundamentally differ between near-field and far-field propagation regimes. Accurately identifying the applicable propagation region is therefore essential for enabling efficient beamforming and channel estimation in ultra-massive MIMO (UM-MIMO) systems. This paper proposes a fully unsupervised learning framework to distinguish near-field from far-field propagation based solely on received signal measurements, before estimating the communication distance, and without relying on channel state information. The proposed approach exploits spatial signal power variations across subarrays of a UM array as a physics-inspired feature extraction stage, followed by the OPTICS clustering algorithm to infer the communication region. Simulation results under various system configurations and signal-to-noise ratio (SNR) levels demonstrate that the proposed method accurately identifies the near-field and far-field regions, showing agreement with theoretical boundaries.

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arXiv2026

Exploiting Mutual Coupling Structure for Channel Estimation of Active RIS-Assisted Links

Accurate channel modeling and estimation of active reconfigurable intelligent surface (RIS)-assisted links with densely integrated elements are essential to fully unleashing this technology's potential. This work adopts a physically consistent model incorporating mutual coupling (MC) effects, modeled via scattering parameters, in RIS-aided communication. We formulate the MC-aware channel estimation as a compressed sensing (CS) problem. The MC effect leads to an increase in the sensing matrix dimensions. This increased dimensionality substantially elevates the complexity of the formulated CS problem. To overcome this, we propose a low-complexity estimator that leverages the structure of the scattering matrix and MC mechanisms to obtain a reduced-size design sensing matrix. Numerical results demonstrate that our approach outperforms MC-unaware estimators by several dBs, achieving accuracy comparable to fully MC-aware solutions but with significantly lower complexity.

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arXiv2026

Reconfigurable Antennas for Next-Generation Wireless Communications: Technologies, Prototypes, Architectures, and Signal Processing

The transition to sixth-generation (6G) networks calls for wireless transceivers with enhanced adaptability and efficiency. Reconfigurable antennas (RAs) have emerged as a promising solution, enabling dynamic control over the electromagnetic properties of individual antenna elements. Their integration into antenna arrays is particularly attractive due to their adaptability and potential for improved energy efficiency. This article provides a comprehensive overview of RA technologies for advanced communication systems, encompassing hardware advancements, early experimental studies, novel system architectures, and key signal processing challenges. From the combined perspective of antenna design and communication operation, we highlight the potential of RAs to enable next-generation wireless communications, while also identifying key challenges and promising research opportunities.

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arXiv2026

Integrated Positioning and Communication via LEO Satellites: A Contemporary Overview

Low Earth orbit (LEO) satellites, as a prominent technology in the sixth generation non-terrestrial network, offer both positioning and communication capabilities. While these two applications have each been extensively studied and have achieved substantial progress in recent years, the potential synergistic benefits of integrating them remain an underexplored yet promising avenue. This article provides a contemporary overview of integrated positioning and communication (IPAC) systems enabled by LEO satellites. Leveraging the distinct characteristics of LEO satellites, we examine how communication systems can enhance positioning accuracy and, conversely, how positioning information can be exploited to improve communication efficiency. A representative case study is included to illustrate enhanced LEO positioning signal acquisition with the assistance of communication-side information. Finally, several key open research challenges for LEO-based IPAC systems are discussed.

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arXiv2026

Orbital Errors in LEO Satellite Positioning: Modeling, Analysis, and Bayesian Calibration

Low-Earth orbit (LEO) satellites offer a promising alternative to global navigation satellite systems for precise positioning. However, their relatively low altitudes make them more susceptible to orbital perturbations, which in turn degrade positioning accuracy. In this work, we study (i) the mechanisms through which orbital errors affect positioning performance and (ii) the fundamental calibration strategies for mitigating low-Earth orbital errors. We first model the satellite orbit and analyze historical two-line element data to characterize the statistical behavior of low-Earth orbital errors. Based on these real-data statistics, we derive the misspecified Cramér-Rao bound to quantify the impact of orbital errors on positioning performance. Subsequently, we develop a Bayesian orbit calibration framework that leverages the learned orbital error statistics as prior information to enhance calibration accuracy. This work sheds light on orbital error modeling, real-data-based evaluation, theoretical impact analysis, and calibration methodologies. Extensive simulations show that stale orbital information can severely degrade positioning performance, whereas learning and incorporating orbital error statistics can effectively enhance orbit calibration and ultimately improve positioning accuracy.

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arXiv2026

Prayer-Gait-Auth: Smartphone IMU-based Behavioral Biometrics from Structured Islamic Prayer Movements

Islamic prayer is a structured movement activity that offers a distinctive setting for behavioral biometrics: all participants execute the same action sequence, so identity must be inferred from differences in execution. We collected inertial data from 95 participants using their own smartphones during nightly congregational Islamic prayer (Taraweeh). A label-aware pipeline converts long recordings into structurally complete two-rakaah behavioral samples (prayer units). This allows us to design unit-level and complete-session behavioral biometrics protocols. To address arbitrary smartphone orientation in worshippers' pockets, we study two motion representations: rotation-invariant magnitudes with gravity-relative acceleration components, and a metadata-aware Qibla-referenced canonicalization that harmonizes platform conventions, reconstructs device-to-world attitude, corrects Android magnetic north to true north via WMM2025, and expresses acceleration and angular velocity in a common Qibla-left-up frame. Under unit-level protocol, the invariant and Qibla-referenced representations reach learned pairwise Random Forest AUC/EER of 0.9932/4.07% and 0.9923/3.96%; under complete-session holdout, 0.9700/7.62% and 0.9618/7.81%. Qibla-frame directional ablations show the complete six-axis representation is strongest overall, with acceleration retaining most learned-verification performance and vertical motion the strongest single-axis cue. A Qibla-referenced SimCLR experiment further yields participant-template AUC 0.9805-0.9817 and EER 5.81-6.24% across two unit-level runs. Signal-, descriptor-, prayer-component ablation analyses show participant identity is distributed across movement dynamics rather than concentrated in one signal or posture. These results establish structured prayer movement as a measurable cross-session behavioral biometric.

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arXiv2026

Tri-Hybrid Multi-User Precoding Using Pattern-Reconfigurable Antennas: Fundamental Models and Practical Algorithms

The integration of pattern-reconfigurable antennas into hybrid multiple-input multiple-output (MIMO) architectures presents a promising path toward high-efficiency and low-cost transceiver solutions. Pattern-reconfigurable antennas can dynamically steer per-antenna radiation patterns, enabling more efficient power utilization and interference suppression. In this work, we study a tri-hybrid MIMO architecture for multi-user communications that integrates digital, analog, and antenna-domain precoding using pattern-reconfigurable antennas. For characterizing the reconfigurability of antenna radiation patterns, we develop two models---Model~I and Model~II. Model~I captures realistic hardware constraints through limited pattern selection, while Model~II explores the performance upper bound by assuming arbitrary pattern generation. Based on these models, we develop two corresponding tri-hybrid precoding algorithms grounded in the weighted minimum mean square error (WMMSE) framework, which alternately optimize the digital, analog, and antenna precoders under practical per-antenna power constraints. Realistic simulations conducted in ray-tracing generated environments are utilized to evaluate the proposed system and algorithms. The results demonstrate the significant potential of the considered tri-hybrid architecture in enhancing communication performance and hardware efficiency. However, they also reveal that the existing hardware is not yet capable of fully realizing these performance gains, underscoring the need for joint progress in antenna design and communication theory development.

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arXiv2026

Coordination of Ground-to-Space Reference Networks for High-Precision GNSS

High-precision Global Navigation Satellite System (GNSS) services rely on accurate orbit, clock, atmospheric, and hardware-bias corrections generated from reference observations. These products are traditionally derived from terrestrial reference networks, whose performance strongly depends on the density and geographic distribution of ground stations. Consequently, sparse or regionally concentrated networks can suffer from tracking gaps and limited global observability, reducing their ability to support globally consistent high-precision products. Low Earth Orbit (LEO) constellations equipped with onboard GNSS receivers and inter-satellite links (ISLs) can serve as a network of spaceborne reference stations, offering a promising way to extend terrestrial reference networks into space, thereby improving observability for ground networks and enabling future direct correction broadcast. However, most existing network-based GNSS correction-generation workflows assume that observations can be centrally collected and processed. This assumption fails in practical ground--to--space architectures, where dynamic satellite geometry and system constraints render communication links intermittent, asymmetric, capacity-limited, and lossy. To address these limitations, this paper proposes a decentralized processing architecture that coordinates the ground-to-space GNSS reference network. By modeling ground stations and LEO satellites as interacting subnetworks over a dynamic graph, our approach allows frequent intra-tier communication while restricting cross-tier exchanges to compact estimation summaries transmitted opportunistically under probabilistic link availability....

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arXiv2026

Physically Consistent Channel Modeling and Signal Processing for Reconfigurable Wireless Systems

Reconfigurable antennas are increasingly integrated into multi-antenna communication systems to exploit large apertures while reducing the hardware complexity, energy consumption, and implementation costs of classical massive arrays. Their reconfigurable electromagnetic (EM) properties, including dynamically varying radiation patterns and state-dependent mutual coupling, challenge the fixed-antenna and decoupled-port assumptions of conventional channel models. This motivates a physically consistent framework connecting Maxwell's equations, circuit theory, and information theory. In this tutorial, we develop a unified framework spanning three coupled dimensions: (i) reconfigurable antenna and transceiver architectures, (ii) physically consistent channel modeling, and (iii) physically consistent signal processing. We first establish a taxonomy covering tunable antennas, reconfigurable transceivers, and emerging array architectures, highlighting their reconfiguration mechanisms and hardware-performance trade-offs. We then develop modeling approaches based on Maxwell's equations, wavenumber-domain representations, multiport network theory, and computational electromagnetics, and use them to construct end-to-end channel and noise models that capture near-field propagation, mutual coupling, and circuit-level impairments. Building on these models, we examine architecture-aware channel estimation, beamforming, data detection, and channel decoding, emphasizing how physical structure reshapes algorithm design and performance-complexity trade-offs. Overall, the tutorial treats physical architecture, channel and noise models, and communication algorithms as coupled components of an end-to-end design, providing a unified foundation for physically consistent reconfigurable wireless systems.

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arXiv2026

Zadoff-Chu Sequences for Chirp-Domain Communication: Diversity-Complexity Tradeoffs in Doubly Dispersive Channels

Orthogonal frequency-division multiplexing (OFDM) combats multipath-induced time dispersion by dividing the channel into narrowband sub-channels. However, when the channel also exhibits frequency dispersion due to mobility (Doppler effect), these sub-channels lose orthogonality and cause inter-carrier interference that degrades the reliability performance of the communication system. We investigate Zadoff-Chu (ZC) sequences for chirp-domain communication to improve reliability in time-frequency dispersive channels. We show that ZC sequences are the only constant-amplitude zero-autocorrelation (CAZAC) sequences that transform a doubly dispersive channel into a singly dispersive channel that is either pure time or frequency dispersion. This transformation is controlled by the ZC root, which provides a geometric projection from the delay-Doppler domain onto a one-dimensional chirp-domain axis with a closed-form design rule. Because the transformed channel is singly dispersive, the receiver equalizes a one-dimensional convolutional channel rather than a two-dimensional delay-Doppler channel and can reuse trellis-based detectors that generate the soft information that coded systems require. Then, we present ZC-based modulations, derive the effective channel after transformation, and analyze diversity, which reveals an underlying trade-off between diversity and receiver complexity. When evaluated at a vehicle speed of 540 km/h and a carrier frequency of 4 GHz, ZC-based modulations demonstrate performance comparable to affine frequency division multiplexing (AFDM) and orthogonal time-frequency space (OTFS), with gains of about 5 dB over orthogonal chirp division multiplexing (OCDM) and 10 dB over OFDM.

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arXiv2026

Analysis of UAV-Enabled IoT Networks with Energy Harvesting and Wake-Up Radio

This paper investigates an unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) architecture that integrates wake-up radio (WuR) and energy harvesting for sustainable device operation. In the proposed system, UAVs transmit radio-frequency (RF) signals that both trigger device activation and replenish stored energy. The IoT device's behavior is modeled as a discrete-time Markov chain, which captures the evolution of its battery level and operational state (asleep or awake), accounting for both energy harvesting and energy consumption. Leveraging stochastic geometry and discrete-time Markov-chain analysis, we develop a comprehensive mathematical framework to assess system performance. We reveal a tradeoff between transmission frequency and energy consumption, and demonstrate that tuning of system parameters, such as UAV density, can significantly improve both energy efficiency and transmission reliability.

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arXiv2026

Malicious Pseudo-Ranging and Localization of Static LOS Wireless Users via Downlink Modulation Classification and Uplink Refinement

The broadcast nature of the wireless medium and openness of wireless standards, e.g., 3GPP releases 16-20, invite adversaries to launch various active and passive attacks on cellular and other wireless networks. This work identifies one such loose end of wireless standards and presents a novel passive attack method enabling an eavesdropper (Eve) to localize a line-of-sight stationary wireless user (Bob) who is communicating with a base station or WiFi access point (Alice). The proposed attack involves two phases. In the first phase, Eve performs modulation classification by intercepting the downlink channel between Alice and Bob. This enables Eve to utilize the publicly available modulation and coding scheme tables to do pesudo-ranging, i.e., the Eve determines the ring within which Bob is located, which drastically reduces the search space. In the second phase, Eve sniffs the uplink channel, and employs multiple strategies to further refine Bob's location within the ring. In simulations, the proposed attack is validated for single-user, multi-user, and multiple-antenna scenarios. Towards the end, we present our thoughts on how this attack can be extended to other scenarios such as non-line-of-sight and mobile users e.g., cars, drones, pedestrians, and how this attack could act as a scaffolding to construct a malicious digital twin map.

cs.IT↗

arXiv2026

Decentralized GNSS at Global Scale via Graph-Aware Diffusion Adaptation

Network-based Global Navigation Satellite Systems (GNSS) underpin critical infrastructure and autonomous systems, yet typically rely on centralized processing hubs that limit scalability, resilience, and latency. Here we report a global-scale, decentralized GNSS architecture spanning hundreds of ground stations. By modeling the receiver network as a time-varying graph, we employ a deep linear neural network approach to learn topology-aware mixing schedules that optimize information exchange. This enables a gradient tracking diffusion strategy wherein stations execute local inference and exchange succinct messages to achieve two concurrent objectives: centimeter-level self-localization and network-wide consensus on satellite correction products. The consensus products are broadcast to user receivers as corrections, supporting precise point positioning (PPP) and precise point positioning-real-time kinematic (PPP-RTK). Numerical results demonstrate that our method matches the accuracy of centralized baselines while significantly outperforming existing decentralized methods in convergence speed and communication overhead. By reframing decentralized GNSS as a networked signal processing problem, our results pave the way for integrating decentralized optimization, consensus-based inference, and graph-aware learning as effective tools in operational satellite navigation.

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