Real-Time Symbol-Domain OFDM Radar in an OpenAirInterface 5G Base Station With O-RAN Sensing Services

This paper presents a real-time orthogonal frequency-division multiplexing (OFDM) radar embedded in the OpenAirInterface (OAI) 5G base-station process. The radar removes communication symbols by regularized element-wise division and performs range-Doppler processing and ordered-statistic constant-false-alarm-rate detection online without modifying the 5G waveform. The implemented system provides 2.57 m nominal range resolution and 0.28 m/s velocity resolution. Hardware measurements identify and mitigate several implementation-specific limitations, most notably a deterministic carrier-dependent transmit-receive phase rotation on a Universal Software Radio Peripheral (USRP) X300. Selecting a tuning-grid-aligned carrier improves mean-removal clutter suppression from -16.4 dB to 38.0 dB and reduces coherent-integration loss from 19.81 dB to 0.27 dB. The measured processing gain closely agrees with its predicted value. Instrumented worker timing confirms real-time operation, with a conservative 58.4 percent utilization bound and no dropped soundings. A custom E2 service model, E2SM-RADAR, exports detections and a compact slow-time product to a near-real-time RAN Intelligent Controller. Live end-to-end operation demonstrates reliable delivery and supports controller-side tracking, micro-Doppler analysis, and classification. With a commercial user equipment connected on the same carrier, measurements show no measurable difference in downlink throughput estimate with sensing enabled, while the radar sensing bandwidth follows the scheduler allocation.

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.

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.

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.

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.

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.

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.

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.

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.

Geometry-Aware Networking for Low-Altitude Economy: Movable Antennas in Space-Air-Ground Integrated Systems

Space--air--ground integrated networks (SAGINs) are emerging as a key foundation for future non-terrestrial networks (NTNs) and low-altitude economy services. However, their performance is increasingly limited not only by communication resources, but by the inability to adapt to rapidly changing spatial geometry. Here, spatial geometry refers to the relative configuration among network nodes, obstacles, and targets, which directly determines propagation conditions, blockage states, interference patterns, and sensing observability.This trend becomes more pronounced as low-altitude operations grow in density and complexity, causing the dominant bottleneck to shift from static resource allocation toward real-time maintenance of favorable spatial geometry across layers.In this article, we argue that movable antenna (MA) technology provides a fundamentally new perspective for SAGIN design. By enabling controlled antenna displacement, MA introduces a spatial degree of freedom that allows the network to directly adapt local spatial geometry at fine granularity, rather than passively reacting to it through beamforming or platform mobility.We present a geometry-aware, layered SAGIN architecture, where Low-Earth-Orbit (LEO) provides macro-scale coverage and coordination, High-Altitude Platform Stations (HAPS) enables regional continuity and backhaul support, and MA is incorporated into the layered design to enable fine-grained geometry adaptation, particularly at unmanned aerial vehicles (UAVs) and terrestrial layers where local channel dynamics are most pronounced. We further discuss how such geometry control enhances robustness, supports multi-functional operation spanning communication, sensing, control, and navigation, and enables more flexible spatial cooperation across layers.