arXiv subjects

Mohamed-Slim Alouini

Publications and source records attributed to Mohamed-Slim Alouini.

At least 37 records · Page 2Linked to original sources

arXiv2026

Agentic AI-Enabled Solar-Powered High-Altitude Platforms for Sustainable SAGINs

Space-Air-Ground Integrated Networks (SAGINs) can extend connectivity, but their communication, computing, and platform operations create tightly coupled energy demands. Solar-powered High-Altitude Platforms (HAPs) offer a promising middle layer by combining persistent regional coverage, renewable-energy harvesting, and onboard computing. However, realizing this potential requires more than optimizing individual links or processors, as radio transmission, task execution, backhaul use, and battery preservation share a common energy budget. Therefore, we introduce a HAP-native Agentic AI framework. It continuously perceives communication, computing, energy, mobility, and mission states; invokes quantitative tools for prediction and verification; and coordinates executable actions through a closed control loop. Then, a multi-timescale design separates fast radio control from task orchestration and long-term energy planning. Furthermore, a disaster-recovery case study illustrates how the framework responds to backhaul congestion, traffic surges, and declining solar generation, improving energy efficiency, task completion, and latency over other baselines. We finally identify trustworthy control, collaborative multi-HAP orchestration, and digital-twin-assisted lifelong adaptation as key steps toward deployable, sustainable, and resilient SAGIN intelligence.

cs.NI↗

arXiv2026

Coverage Analysis of Large-Scale HAPS Networks Using Directional Beams

High-altitude platform stations (HAPS) are pivotal in next-generation wireless networks for reducing core network burdens and enabling cost-effective communication. In this article, we propose a spherical stochastic geometry-based analytical framework for the coverage performance evaluation of HAPS networks. Considering the significant influence of directional antenna gain on interference evaluation, we analyze coverage performance under a general channel model that accommodates various beam patterns. Analytical expressions of the uplink and downlink coverage probabilities in cellular and cell-free networks are provided respectively and their accuracy are verified by Monte Carlo simulation. Furthermore, the influences of network-level and physical-level parameters on coverage probability are studied. Finally, several factors that align with the analytical framework of this article are discussed.

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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

Uplink MIMO Performance Analysis for Diverse HAPS Antenna Array Architectures

High-altitude platform stations (HAPS) are promising components of 6G and beyond networks, where antenna array configuration is critical for achieving wide-area coverage and high capacity with massive MIMO. This paper investigates and compares the uplink signal-to-interference-plus-noise ratio (SINR) distributions of user equipments (UEs) for five antenna array structures, including the cylindrical antenna array, the 3GPP antenna array, the hemispherical antenna array, and two proposed architectures, namely the truncated cone and truncated hemispherical antenna arrays, under uniform, Gaussian, and Poisson cluster process UEs distributions. Simulation results show that both proposed arrays achieve performance comparable to the hemispherical array, with the truncated hemispherical array being particularly effective for densely distributed UEs, while the truncated cone array offers a favorable tradeoff between performance and implementation complexity.

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arXiv2026

Joint Beamforming, Energy Management, and Trajectory Optimization for Figure-Eight Loitering in Solar-Powered HAPS-Enabled ISAC Systems

Solar-powered high-altitude platform stations (HAPSs) provide a promising platform for integrated sensing and communication (ISAC) owing to their wide-area coverage and long-endurance operation. This paper proposes a solar-powered HAPS-enabled ISAC framework for sustainable day-night operation, where a figure-eight loitering architecture is adopted to provide persistent ISAC services over geographically separated regions while harvesting solar energy. A unified communication-sensing-energy model is developed by jointly characterizing solar energy harvesting, battery dynamics, propulsion power consumption, communication transmission, and synthetic aperture radar (SAR) imaging. Based on this model, coupled optimization problems are formulated for daytime operation (DTO) and nighttime operation (NTO), where the battery state bridges the two operational phases through a long-term energy budget. The proposed framework jointly optimizes communication, sensing, mobility, and energy management to maximize daytime communication performance while minimizing nighttime propulsion energy consumption. Efficient iterative algorithms are developed to solve the resulting non-convex optimization problems. Simulation results verify the effectiveness of the proposed communication-sensing-energy co-design and demonstrate that the proposed framework effectively supports sustainable day-night ISAC operation.

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arXiv2026

Performance Evaluation of RF-powered IoT in Rural Areas: The Wireless Power Digital Divide

Bridging the digital divide is one of the goals of mobile networks in the future, and further building IoT networks in rural areas is a feasible solution. This paper studies the downlink performance of rural wireless networks, where IoT devices we consider are battery-less and powered only by ambient radio-frequency (RF) signals. We model a rural area as a finite area that is far from the city center. The base stations (BSs) in the whole city and the access points (APs) in the finite network both act as sources of wireless RF signals harvested by IoT devices. We assume that BSs follow an inhomogeneous Poisson Point Process (PPP) with a 2D-Gaussian density, and a fixed number of APs are uniformly distributed inside the finite area following a Binomial Point Process (BPP). The IoT devices we consider can harvest energy and receive downlink signals in each time slot, which is divided into two parts: (1) a charging sub-slot, where the RF signals from BSs and APs are harvested by IoT devices, and (2) a transmission sub-slot, where each IoT device uses the harvested energy to receive and process downlink signals. We consider two main system requirements: minimum energy requirement and signal-to-interference-plus-noise ratio (SINR). Using these two parameters, we investigate the overall coverage probability (OCP) related to them. We first study the effect of remoteness in rural areas on energy harvesting performance. Then we analyze the influence of IoT device's location and the number of APs on coverage probability when the effect of BSs can be ignored. This paper shows that the IoT devices located inside the rural area can obtain about twice the ECP and OCP of IoT devices located near the edge. For the average downlink performance in rural areas with radii less than 100 m, more than 80% of the RF-powered IoT devices can be supported when there are 100 APs deployed.

cs.NI↗

arXiv2026

HAPS-enabled Downlink Coverage Enhancement in Islands and Maritime Areas

Non-terrestrial networks (NTNs) are poised to play a critical role in next-generation mobile communications, offering enhanced flexibility, improved line-of-sight (LoS) conditions, and overcoming the limitations of terrestrial networks (TNs). Among NTN platforms, high altitude platform stations (HAPSs) have emerged as a promising solution to provide Internet connectivity to underserved regions, including rural areas, islands, and maritime zones, where traditional infrastructure deployment is costly and challenging to deploy. In this paper, we investigate the feasibility of large-scale HAPS deployment to connect island and maritime users, considering real-world shadowing effects on part of HAPSs caused by the presence of island building clusters. We first analyze the coverage performance of onshore (island) and offshore (remote sea) users, in which the channels between HAPSs and the user follow the shadowed Rician distributions and Rician distributions, respectively. Next, we introduce an evaluation method for nearshore users in a hybrid channel environment with HAPSs, and propose approximations that can reduce computational complexity. Based on the simulation results, we discuss how the distance from the island boundary (i.e. the relative remoteness of maritime users) affects coverage performance under different HAPS densities. We also emphasize the importance of choosing a balanced HAPS density or an advanced HAPS deployment scheme.

cs.NI↗

arXiv2026

Three-Dimensional Spatial Correlation Modeling for Cylindrical mMIMO Arrays in HAPS

High-altitude platform stations (HAPS) are envisioned as a key component of future wireless networks, enabling ultra-wide coverage and providing direct connectivity to users with cylindrical massive multiple-input multiple-output (mMIMO) systems. Exploiting the channel degrees of freedom necessitates accurate modeling and characterization of three-dimensional (3D) channels in the presence of spatial correlation functions (SCFs). However, existing spatial correlation models are primarily developed for planar or linear antenna arrays and cannot be directly applied to cylindrical geometries commonly adopted by HAPS platforms. To address this limitation, this paper derives an exact closed-form expression for the SCF of 3D MIMO channels with antenna elements arranged in a cylindrical array. The proposed formulation is based on the spherical harmonic expansion (SHE) of plane waves and accommodates arbitrary antenna radiation patterns and angular distributions through the Fourier series (FS) coefficients of the power azimuth and zenith spectra. The derived SCF is validated through Monte Carlo simulations under standard-compliant settings.

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arXiv2026

Signal from Space: Detecting Schools and Towers to Bridge the Digital Divide

Reliable internet access is essential for modern education, yet millions of school-aged children especially in developing regions remain offline due to unconnected schools. The Giga Initiative aims to connect every school to the internet, but doing so at scale requires efficient methods to map schools and assess surrounding connectivity infrastructure without relying on sparse or noisy third-party datasets. In this work, we propose a scalable, vision-only framework that uses high-resolution satellite imagery and transfer learning to address both tasks simultaneously. By adapting pre-trained object detection models to new geographical regions with minimal labeled data, we detect schools and cell towers directly from space. We then analyze the spatial relationship between detected schools and nearby towers as a proxy for connectivity availability. This purely imagery-driven pipeline enables large-scale infrastructure mapping, reduces dependency on auxiliary data, and supports data-driven prioritization of connectivity investments in underserved areas. Our approach is demonstrated on real satellite imagery from Lesotho, showing strong performance across this region.

cs.CV↗

arXiv2026

MJSAC: McCormick Relaxation-based Waveform Design for Joint Sensing and Communication

In the upcoming 5G Advanced and 6G technologies, joint sensing and communication (JSAC) will play a pivotal role in enabling the simultaneous utilization of hardware and spectrum resources for communication and sensing tasks. While current algorithms primarily focus on designing beampattern invariant covariance matrices for transmitting various symbols for communication, they often overlook the distances among these symbols. While these covariance matrices effectively facilitate ranging operations, they have adverse effects on communication performance. Designing beampattern invariance covariance matrices with maximal distances among themselves poses a challenging non-convex problem. In this paper, we introduce a novel waveform design method based on McCormick relaxation called McCormick-based JSAC (MJSAC). MJSAC sequentially solves an optimization problem to generate a set of covariance matrices by maximizing the distances (Frobenius norm) among themselves while ensuring a consistent beam pattern. Also, MJSAC eliminates the requirement for channel information to generate the covariance matrices. Through simulations, we demonstrate that MJSAC outperforms conventional algorithms, even those utilizing channel information at the transmitter.

cs.IT↗

arXiv2026

From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks

In this article, we introduce a novel low-altitude wireless network (LAWN), which is a reconfigurable, three-dimensional (3D) layered architecture. In particular, the LAWN integrates connectivity, sensing, control, and computing across aerial and terrestrial nodes that enable seamless operation in complex, dynamic, and mission-critical environments. Different from the conventional aerial communication systems, LAWN's distinctive feature is its tight integration of functional planes in which multiple functionalities continually reshape themselves to operate safely and efficiently in the low-altitude sky. With the LAWN, we discuss several enabling technologies, such as integrated sensing and communication (ISAC), semantic communication, and fully-actuated control systems. Finally, we identify potential applications and key cross-layer challenges. This article offers a comprehensive roadmap for future research and development in the low-altitude airspace.

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arXiv2026

A Spherical Stochastic Geometry Framework for Patrol-Based HAPs Network: Coverage and Energy Efficiency Analysis

This paper develops a stochastic-geometry framework for high-altitude platform station (HAPs) networks in which platforms execute cyclic patrol trajectories anchored to designated service regions. We introduce two small-circle ring Cox process models on the spherical Earth. In the small-circle ring Poisson Cox process (SCR-PCP), platforms form one-dimensional Poisson point processes on localized patrol rings, whereas in the small-circle ring binomial Cox process (SCR-BCP), each ring contains a fixed number of uniformly distributed platforms. We establish the isotropy of both models and derive spatial statistics, including the distributions of the nearest-anchor, nearest-ring, and nearest-HAPs distances, together with the joint serving distance and serving ring angle distribution required for SCR-BCP analysis. Building on these results, we derive coverage probability expressions under nearest-HAPs association by decomposing aggregate interference into same-ring and other-ring components and characterizing their conditional Laplace transforms. To account for the flight dynamics of patrol-based HAPs, we integrate a steady circular flight propulsion model with the communication analysis and introduce a coverage energy efficiency (CEE) metric. This yields an analytical condition for the energy-optimal patrol radius that balances coverage performance against the propulsion cost of circular flight. Numerical results reveal fundamental differences between intensity-driven (SCR-PCP) and finite-fleet (SCR-BCP) deployments and demonstrate that patrol geometry, platform density, and cruising velocity should be jointly optimized to achieve energy-efficient HAPs operation.

cs.IT↗

arXiv2026

Joint 3D Trajectory and Power Allocation for HAPs-UAV Bistatic ISARAC in Low-Altitude Networks

This paper investigates joint three-dimensional (3D) trajectory planning and resource allocation for a high-altitude platform (HAPs)-unmanned aerial vehicle (UAV) bistatic integrated synthetic aperture radar (SAR) and communication (ISARAC) system in low-altitude networks. In the proposed architecture, the HAPs provides persistent wide-area connectivity by transmitting ISARAC waveforms for ground-user communications, while a low-altitude UAV exploits its proximity and mobility to passively collect ground-target echoes for high-resolution SAR imaging. We formulate a sum-rate maximization problem for ground users subject to stringent SAR imaging signal-to-noise ratio (SNR) and resolution requirements, a total energy budget for ISARAC transmission, and UAV dynamic constraints. The resulting problem is inherently nonconvex. To tackle it, an alternating optimization (AO) framework is developed, where the power-allocation subproblem with fixed UAV states admits a closed-form water-filling solution, while the UAV trajectory optimization with fixed transmit powers is handled via successive convex approximation (SCA) and difference-of-convex (DC) programming. Simulation results verify the effectiveness of the proposed approach and demonstrate its capability to jointly support persistent communication coverage and high-resolution sensing in low-altitude network scenarios.

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arXiv2026

Low-Altitude Wireless Networks: The Next Horizon of Wireless Infrastructure

Low-altitude airspace, roughly defined as the region up to 3000 meters above ground level, is envisioned as a new spatial domain for daily human and machine activities. This article introduces the concept of the Low-Altitude Wireless Network (LAWN), which represents a paradigm shift from the current ground-based communication-only network to a three-dimensional (3D) multifunctional network. We analyze the key driving forces, network architecture, and limiting factors of LAWN, with a particular focus on the tight integration of communication, sensing, and control in highly dynamic airspace environments. By establishing the coupling between airspace capacity and wireless channel capacity, we reveal the intrinsic limits of airspace management and identify the fundamental challenges and opportunities associated with its evolution.

cs.NI↗

arXiv2026

Wi-Fi HaLow (IEEE 802.11ah) for Long-Range Monitoring Links: Point-to-Point NLoS/LoS and LoS Mesh Field Characterization

Monitoring deployments often require reliable long-range wireless links to intermittently upload sensor logs and short video snapshots. Wi-Fi HaLow (IEEE~802.11ah) is a promising candidate due to sub-1 GHz propagation and bandwidth-flexible PHY modes. This summary paper reports a field characterization organized around three deployment-driven regimes: (i) point-to-point Non-Line-of-Sight (NLoS) links; (ii) point-to-point Line-of-Sight (LoS) links over several-hundred-meter distances; and (iii) LoS mesh networking with fixed relay nodes for range extension. Using commodity HaLow dongle-class nodes in all regimes, we report application-layer goodput and monitoring-centric update latency based on transferring a representative ``heavy'' object (a $\sim$30 s video file). The measurements reveal (a) a clear bandwidth--range tradeoff and an NLoS coverage boundary around $\sim$120 m, (b) gradual throughput decay under LoS up to 814 m in single-hop with 0.15 Mbps at the farthest point, and (c) kilometer-class extension under LoS when fixed relays are introduced, reaching 901 m (two fixed relays) and 1110 m (three fixed relays

cs.NI↗

arXiv2026

CRB-Based Resource Allocation in Multi-User Uplink Transmissions

In this work, we study the design of receivers for uplink multi-user systems, aiming to estimate both the channel and the transmitted symbols. We consider two estimation strategies: (i) a joint estimation approach, where the channel and symbols are estimated simultaneously, and (ii) a sequential estimation approach, where the channel is first estimated and then used for symbol detection. For both strategies, we derive the Cramér-Rao Bound (CRB) for symbol estimation to characterize fundamental performance limits. When efficient receivers achieving the CRB exist, these bounds provide accurate lower bounds on the mutual information. In general, however, such receivers may not be available, and we instead use these same CRB-based metrics as practical proxies for achievable throughput. Leveraging tools from random matrix theory (RMT), we analyze the asymptotic behavior of these lower bounds under various asymptotic regimes for both estimation strategies. This analysis enables the derivation of generic power allocation guidelines that asymptotically maximize the proxy metrics. Simulation results confirm the accuracy of the asymptotic expressions and their effectiveness in guiding resource allocation decisions.

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arXiv2026

Label-Efficient School Detection from Aerial Imagery via Weakly Supervised Pretraining and Fine-Tuning

Accurate school detection is essential for supporting education initiatives, including infrastructure planning and expanding internet connectivity to underserved areas. However, many regions around the world face challenges due to outdated, incomplete, or unavailable official records. Manual mapping efforts, while valuable, are labor-intensive and lack scalability across large geographic areas. To address this, we propose a weakly supervised framework for school detection from aerial imagery that minimizes the need for human annotations while supporting global mapping efforts. Our method is specifically designed for low-data regimes, where manual annotations are extremely scarce. We introduce an automatic labeling pipeline that leverages sparse location points and semantic segmentation to generate infrastructure masks from which we generate bounding boxes. Using these automatically labeled images, we train our detectors on a first training stage to learn a representation of what schools look like, then using a small set of manually labeled images, we fine-tune the previously trained models on this clean dataset. This two stage training pipeline enables large-scale and strong detection in low-data setting of school infrastructure with minimal supervision. Our results demonstrate strong object detection performance, particularly in the low-data regime, where the models achieve promising results using only 50 manually labeled images, significantly reducing the need for costly annotations. This framework supports education and connectivity initiatives worldwide by providing an efficient and extensible approach to mapping schools from space. All models, training code and auto-labeled data will be publicly released to foster future research and real-world impact.

cs.CV↗

arXiv2026

Frequency Diverse Arrays: Fundamentals, Key Insights, and Future Directions

Frequency diverse arrays (FDA) have attracted sustained interest as a promising architecture for introducing range-dependent responses into array systems. Unlike conventional phased arrays (PA), whose transmit behavior is primarily angle-dependent, FDA employs inter-element frequency offsets to generate time-and range-dependent phase structures, thereby producing a joint time-range-angle array response. Despite extensive research, the physical meaning of FDA-induced degrees of freedom remains debated, particularly in relation to range-angle coupling, the feasibility of time-invariant focusing, and the distinction between frequency-driven and waveform-driven range selectivity. This paper reexamines FDA from a structural and manifold-based perspective. A central contribution is the introduction of an irreducibility criterion, which distinguishes genuine range-domain physical degrees of freedom from effects that can be reproduced by equivalent signal-processing transformations. Based on this perspective, PA, multiple-input multiple-output (MIMO), FDA, and FDA-MIMO are comparatively interpreted according to the physical origin of their effective degrees of freedom, including spatial phase, waveform orthogonality, frequency gradients, and their interaction. The paper further clarifies the role of frequency across different array paradigms, contrasts FDA with time-coding-based architectures, and explains how key FDA properties such as manifold expansion, range--angle coupling, time variation, and multi-frequency diversity translate into system capabilities. Building on these structural insights, the paper connects FDA to a broad range of radar and communication functionalities, including parameter estimation, target detection, imaging, physical-layer security, and integrated sensing and communication.

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