Mohamed-Slim Alouini

Localization in wireless networks is traditionally performed through centralized architectures that process measurements at a single node. However, such architectures not only lead to a high computational burden but also suffer from limited robustness, as the failure of the central node terminates the localization operation. They also require extremely high communication bandwidth, as all nodes must transmit their measurements to a central server. Moreover, assuming prior knowledge of each target node's transmit power is impractical, as it varies with battery level, hardware imperfections, and antenna orientation. To address these issues, this paper proposes a Distributed Cooperative Localization technique (DCLU), which jointly estimates the location and transmit power of the nodes. The joint maximum-likelihood formulation is non-convex and discontinuous, and cannot be solved using off-the-shelf optimization techniques. We address this challenge by constructing a convex approximation of the ML objective, which enables efficient distributed estimation using a coordinate descent algorithm. We further establish theoretical guarantees for convergence, including the rate of convergence. Compared with centralized approaches, DCLU offers enhanced scalability and robustness. We also derive the Cramer-Rao lower bound (CRLB) as a performance benchmark. Extensive simulations and on-field experiments demonstrate that DCLU achieves higher localization accuracy while significantly reducing computational complexity relative to existing techniques.

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.

The increasing proliferation of internet-connected devices, together with the widespread reliance on wireless communication in daily activities, are limiting the capabilities of the radio frequency (RF) spectrum to meet data traffic demands. In particular, the rise in connectivity requirements to realize next generation networks, coupled with the growth in utilization of artificial intelligence (AI) and its associated transmission of massive amounts of data, contribute to the need to leverage the optical spectrum to complement the already-congested RF bands. Optical wireless connectivity encompasses a range of solutions that can support data transfer in various use cases, ranging from intra-chip interconnects to deep-space communication. They benefit from advantages in terms of operation within a broad unlicensed spectrum, minimization of RF interference, and inherent security. On the other hand, optical wireless communication (OWC) systems might experience a decline in performance as a result of factors like atmospheric conditions and link blockage. This requires customizing system designs according to the considered use cases and investigating new solutions for realizing optical wireless connectivity. In this article, we present an overview of OWC, encompassing both its conceptual frameworks and the scientific and technological advancements that hold the potential for shaping its future in next generation networks.

The Terahertz (THz) band (0.1-10 THz) attracts attention due to its massive bandwidth, potentially enabling ultra-high data rate with ultra-low latency. High-altitude platform stations (HAPSs) are envisioned as nodes of a non-terrestrial network (NTN) that complements terrestrial networks, particularly for connecting the unconnected and addressing temporary needs, such as disaster relief. THz HAPS communication is a promising solution for NTN, since HAPS is quasi-stationary in nature, located in the stratosphere, thereby avoiding beam misalignment and molecular absorption loss issues in the THz band. In this paper, we provide an overview of THz band HAPS communication, considering HAPS-to-ground, HAPS-to-HAPS, HAPS-to-satellite, and HAPS-to-unmanned aerial vehicle links. We present simulation results of outage probability, ergodic capacity, and spectral efficiency under beam misalignment fading and turbulence fading in comparison to millimeter wave and sub-6 GHz bands. In addition, we summarize existing THz HAPS studies to reveal ongoing efforts. Moreover, we discuss open issues and research directions for THz band HAPS-related links. Overall, this paper draws a clear picture of THz HAPS communication by reporting its potential and challenges.

Maritime communication demands have surged due to diverse applications such as transoceanic shipping, offshore resource exploration, and emergency rescue. However, current communication systems are unable to meet the diverse data rate demands of maritime applications. In this paper, we propose a space-air-ground-sea integrated network (SAGSIN) that combines onshore base stations, uncrewed aerial vehicles, high-altitude platform stations, and low Earth orbit satellites to provide seamless and reliable communication across vast oceanic regions. Based on stochastic geometry, we model the spatial distribution of network devices on spherical surfaces. The Matérn hard-core point process (MHCPP) is used to model the deployment of both ground and aerial devices, considering the minimum spacing to avoid overdense and collisions. Aerial relays are used to extend the coverage of coastal base stations, with relay selection aimed at maximizing the end-to-end channel capacity. Analytical expressions for the uplink coverage probability are derived by considering the receiver’s decoding threshold. Furthermore, the analysis is extended to the communication probability by incorporating quality of service (QoS) with data rate as the metric. Numerical results validate the accuracy of the proposed model and demonstrate that SAGSIN can effectively extend reliable communication from coastal regions to the deep ocean. Furthermore, dynamic QoS scheduling across heterogeneous links, each with different transmission capabilities, can enhance overall system efficiency to meet diverse QoS demands.