Tareq Y. Al-Naffouri

This paper introduces a novel, noninvasive, and contactless method for dehydration measurement using a frequency-modulated continuous wave (FMCW) radar operating at center frequencies of 10 GHz and 77 GHz. The proposed framework begins with a customized preprocessing technique designed to isolate the signal corresponding to the target of interest (ToI) from the received radar data, ensuring that the extracted features reliably reflect physiological indicators of hydration status. To capture the complex, nonlinear patterns associated with dehydration, a wavelet scattering transform (WST)-based feature extraction method was employed, which provides robust input for machine learning models. These features are then used to train and evaluate various classifiers, the best one being neural networks (NN), which accurately classify protocol sessions. A key challenge in nonclinical dehydration assessment is the absence of biochemical ground truth. To overcome the challenge, we developed and annotated a novel dataset—the Fasting Dataset—collected under controlled conditions from individuals who observed fasting. This dataset captures radar measurements at different physiological stages, offering a valuable reference for hydration-level analysis. Validation using this custom dataset demonstrates the initial feasibility of the proposed methodology for real-time, noninvasive, and contactless dehydration monitoring in remote healthcare settings.

Transportation mode recognition plays a pivotal role in urban planning, environmental research, and smart mobility services. This study introduces a novel data-driven framework leveraging radio access network (RAN) measurements from cellular infrastructure to classify four modes of transportation-car, bus, walk, and train-with high accuracy. Unlike conventional methods that rely on GPS or motion sensors, which are power-intensive and raise privacy concerns, our approach capitalizes on the widespread availability of cellular infrastructure to build the cellular signal features dataset used for classification, ensuring a more energy-efficient, non-intrusive, and scalable solution. Data was collected across two urban cities and a rural university campus, capturing realistic mobility patterns. The proposed system employs gradient-boosted decision trees for classification, achieving a high average $F_{1}$-score of $93.7\%$. By relying on user-independent inference, the model demonstrates robust performance without requiring personalized data. The results highlight the potential of cellular network data for real-time, privacy-preserving transportation mode detection in support of smart mobility systems.

his letter studies hybrid far- and near-field channel estimation for terahertz (THz)-band ultra-massive multiple-input multiple-output (UM-MIMO) systems in the presence of multi-user interference caused by non-orthogonal pilots. Specifically, we propose a two-stage model-driven estimator that modifies both the linear and nonlinear components of the traditional orthogonal approximate message passing (OAMP) algorithm by incorporating U-Net convolutional neural networks following the fixed-point principle for the nonlinear steps and by updating the decorrelated linear estimator matrix based on the channel correlation statistics. Simulation results verify that the proposed algorithm effectively mitigates interference from users with non-orthogonal pilots and still outperforms baseline algorithms by at least 1 dB, in terms of channel-estimation accuracy.

This paper introduces an innovative error feedback framework designed to mitigate quantization noise in distributed graph filtering, where communications are constrained to quantized messages. It comes from error spectrum shaping techniques from state-space digital filters, and therefore establishes connections between quantized filtering processes over different domains. In contrast to existing error compensation methods, our framework quantitatively feeds back the quantization noise for exact compensation. We examine the framework under three key scenarios: (i) deterministic graph filtering, (ii) graph filtering over random graphs, and (iii) graph filtering with random node-asynchronous updates. Rigorous theoretical analysis demonstrates that the proposed framework significantly reduces the effect of quantization noise, and we provide closed-form solutions for the optimal error feedback coefficients. Moreover, this quantitative error feedback mechanism can be seamlessly integrated into communication-efficient decentralized optimization frameworks, enabling lower error floors. Numerical experiments validate the theoretical results, consistently showing that our method outperforms conventional quantization strategies in terms of both accuracy and robustness.

Low Earth orbit (LEO) satellite constellations are rapidly becoming essential enablers of next-generation wireless systems, offering global broadband access, high-precision localization, and reliable sensing beyond terrestrial coverage. However, the inherent limitations of individual LEO satellites, including restricted power, limited antenna aperture, and constrained onboard processing, hinder their ability to meet the growing demands of 6G applications. To address these challenges, this article introduces the concept of distributed integrated sensing, localization, and communication (DISLAC) over LEO constellations, inspired by distributed multiple input multiple output architectures. By enabling inter-satellite cooperation through inter-satellite links, DISLAC jointly exploits communication, localization, and sensing functionalities, achieving synergistic gains in throughput, positioning accuracy, and sensing robustness through shared resources and cooperative design. We present illustrative case studies that quantify these benefits and analyze key system-level considerations, including synchronization, antenna reconfigurability, and inter-satellite link design. The article concludes by outlining open research directions to advance the practical deployment of DISLAC in future non-terrestrial networks.

We investigate a multi-low Earth orbit (LEO) satellite system that simultaneously provides positioning and communication services to terrestrial user terminals. To address the challenges of accurately acquiring channel state information in LEO satellite systems, we propose a novel two-timescale positioning-aided channel estimation framework, exploiting the distinct variation rates of position-related parameters and channel gains inherent in LEO satellite channels. Using the misspecified Cramér-Rao bound (MCRB) theory, we systematically analyze positioning performance under practical imperfections, such as inter-satellite clock bias and carrier frequency offset. Furthermore, we theoretically demonstrate how position information derived from downlink positioning can enhance uplink channel estimation accuracy, even in the presence of positioning errors, through an MCRB-based analysis. To address the limited link budgets and communication rates of single-satellite communication, we develop a multi-LEO cooperative beamforming strategy for downlink transmission that leverages cluster-wise satellite cooperation while maintaining reduced complexity. Theoretical analyses and numerical results confirm the effectiveness of the proposed framework in facilitating high-precision downlink positioning under practical imperfections, facilitating uplink channel estimation, and enabling efficient downlink communication.

The terahertz (THz) band is a key enabler for future wireless systems, promising ultra-high data rates and dense spatial reuse. However, the reliability of THz links remains a major challenge due to severe path loss and small-scale fading effects, particularly in dynamic indoor and outdoor environments. This paper presents a comprehensive diversity analysis framework for THz communication systems under small-scale fading conditions. We model fading statistically using the generalized α-μ distribution for indoor scenarios and the mixture of gamma (MG) model for outdoor propagation. We complement previous works that analyzed diversity under the α-μ channels 1, 2. In particular, we present new insights on diversity for the MG channel in addition to recovering the results of 2 using a different approach. Moreover, we derive asymptotic expressions for the bit error rate as a function of the inverse signal-to-noise ratio, recovering all of the α-μ diversity results using a simpler approximation method. The analytical results are extensively validated through Monte Carlo simulations, demonstrating excellent agreement. Our findings show that diversity gains in THz systems are strongly influenced by the number of independent paths, the severity of fading, and frequency selectivity. The proposed framework provides system designers with clear guidelines for quantifying and optimizing diversity gains in emerging channel models, paving the way for more reliable high-frequency wireless links in next-generation networks.

Federated Learning (FL) enables decentralized model training across distributed clients while preserving data privacy. However, its effectiveness can degrade in the presence of non-independent and identically distributed (IID) data distributions, unreliable wireless communication, and adversarial behavior such as label-flipping attacks. In this letter, we propose RANGE-CFL, a robust and network-aware framework for Clustered Federated Learning that addresses these challenges. RANGE-CFL integrates a network-aware aggregation scheme to mitigate communication unreliability and introduces a dynamic clustering mechanism with participation memory to maintain group coherence under intermittent connectivity. To defend against label-flipping attacks, we develop a gradient-based client filtering method that detects and removes malicious clients using PCA-assisted clustering over output neuron gradients. Experimental results on the EMNIST and CIFAR10 datasets show that RANGE-CFL enhances global model accuracy while simultaneously reducing inter-user accuracy variance. This demonstrates its robustness against label-flipping attacks and its effectiveness in promoting fairness and stability across heterogeneous user data distributions, outperforming the baseline FL algorithm.