rf measurement techniques

This study addresses reliability challenges in maritime wireless communications under shared-spectrum conditions, focusing on signal degradation induced by dynamic obstructions and complex marine environments. We extend laboratory-based electromagnetic propagation measurement methodologies—previously confined to controlled settings—to real-world deployments on an electric research vessel, conducting systematic indoor–outdoor comparative experiments. These include line-of-sight obstruction tests, multi-location received signal strength (RSS) measurements, and empirical signal attenuation analysis. We quantitatively characterize the impact of obstacles (e.g., lab objects and ship structures), transmission distance, and device mounting height on path loss. Results reveal that obstructions significantly exacerbate path loss; distance and antenna height exhibit nonlinear effects on link margin; and typical ship structures introduce up to 20 dB additional attenuation. The findings provide reproducible empirical evidence and actionable design guidelines for robust maritime wireless network deployment.

This work addresses the limitations of existing RF measurement platforms, which rely on laboratory equipment or fixed infrastructure and thus lack the flexibility required for prolonged field-based spectrum monitoring. The authors propose a portable, battery-powered RF acquisition system integrating a HackRF One software-defined radio, a Raspberry Pi 5, a GNSS receiver, and a high-speed SSD to enable continuous IQ data recording with precise spatiotemporal metadata. Data are stored in the SigMF format at sustained write throughput of 75–85 MB/s without sample loss, while GNSS synchronization achieves timing errors under one second and meter-level positioning accuracy. Field experiments successfully captured characteristic propagation effects at 2.45 GHz—including vegetation attenuation, urban multipath, and indoor interference—demonstrating significantly enhanced deployment flexibility, environmental adaptability, and data fidelity for real-world RF sensing.

This study addresses the lack of empirical channel models for centimeter-wave (cmWave) frequencies (6.9–14.5 GHz, FR3 band) in complex commercial office environments. We conduct the first systematic measurement campaign and statistical channel modeling across multiple office floors, covering representative indoor spaces—including workstations, meeting rooms, corridors, and laboratories—and quantify key propagation parameters: path loss, shadow fading, delay spread, and angular spread. Based on the measurements, we propose a generic, measurement-based indoor channel model tailored to cmWave/FR3 bands, revealing high-frequency signal attenuation trends and spatial dispersion characteristics in realistic office settings. The resulting model provides an empirically grounded, reusable framework to support high-frequency indoor network deployment, link budget optimization, and MIMO system design. It significantly improves the accuracy of wireless performance prediction and enhances the reliability of network planning for commercial buildings.

To address the urgent demand for high-resolution, high-sensitivity, and low-latency channel probing in multi-scenario cellular communications—including indoor/outdoor environments, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links, and integrated sensing and communication (ISAC)—this paper designs and implements an adaptive channel probing system for centimeter-wave (cmWave) and FR3 frequency bands. The system incorporates high-precision time-delay resolution, wide-dynamic-range path-loss measurement, and a reconfigurable RF front-end architecture to enable rapid acquisition of bidirectional angular and omnidirectional angle-delay spectra. It achieves an unprecedented 2.5 ns time-delay resolution, a 170 dB path-loss dynamic range, and completes full 360° power-angle-delay spectrum measurement within 0.9 ms. Furthermore, the system supports cross-band rapid reconfiguration, significantly enhancing sensing accuracy and experimental flexibility in complex propagation environments.

This work proposes a cross-layer, interpretable performance diagnosis method to address the challenge of detecting subtle radio-layer dynamic anomalies in O-RAN systems when end-to-end latency appears stable. Leveraging real-world measurements across multiple distances and user equipment (UE) types, the approach jointly analyzes application-layer tail latency—such as the 95th percentile—with radio-layer metrics including scheduling behavior, modulation and coding scheme (MCS), block error rate (BLER), and signal quality to construct lightweight “degradation flags.” The method enables non-intrusive yet effective detection of radio-layer performance degradation, revealing the sensitivity of tail latency to UE type, distance, and network load. This facilitates practical and efficient fault localization and monitoring in O-RAN deployments.

This study addresses a critical gap in communication-aware robotic planning, where existing approaches commonly rely on channel-level metrics to predict end-to-end 5G throughput—a practice lacking empirical validation in private 5G deployments. Conducted in a shielded underground industrial environment, the work integrates commercial ray-tracing simulations, Gaussian process regression with a rational quadratic kernel, a mobile robotic platform, and off-the-shelf 5G user equipment to perform real-world measurements. It reveals for the first time that dynamic adaptation of MIMO spatial layers is the primary cause of systematic overestimation of throughput by conventional channel models, with ray tracing significantly overpredicting performance even in line-of-sight conditions. In contrast, a data-driven approach that directly learns end-to-end throughput reduces prediction error by approximately two-thirds and exhibits near-zero bias, demonstrating clear superiority over traditional channel-centric modeling.

This work addresses the challenge of achieving high-sensitivity motion detection in low-multipath outdoor environments, where conventional Wi-Fi sensing methods suffer from device-induced phase offsets and noise. The authors propose a model-driven, low-complexity approach that uniquely leverages the structural properties of channel state information (CSI) phase combined with a phase averaging mechanism to effectively suppress phase errors and enhance signal-to-noise ratio. By transforming the typically detrimental low-multipath condition into an advantage, the method enables robust detection of subtle movements at range. Using compressed beamforming frames collected from off-the-shelf 802.11ac devices, the system successfully detects crows flying several meters away in a real orchard environment and remains largely unaffected by vegetation clutter when wind speeds are below 3 m/s, significantly improving both sensitivity and robustness for distant micro-motion targets.

This work addresses the limited accuracy of conventional radiomap modeling at high frequencies, which stems from neglecting fine-scale scatterers. The authors propose a multi-scatterer channel model based on spherical wave modal expansion that unifies the characterization of source radiation, single scattering, and multiple-scattering coupling effects through modal superposition. By reformulating the forward model as an inverse optimization problem, the approach jointly estimates scatterer responses and transmitter location. Notably, it integrates multi-scattering interactions with low-order modal approximation within a physically interpretable framework—a first in the field—and enables high-fidelity radiomap reconstruction and extrapolation from sparse measurements. Simulations demonstrate that the proposed model significantly outperforms existing methods in both spatial and beam domains, particularly in dense scattering environments.