WIRELESSLY COORDINATED OPEN-LOOP COHERENT DISTRIBUTED ANTENNA ARRAYS USING SOFTWARE-DEFINED RADIO
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Engineering - Electronic and electrical engineerin↗
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Passband Signal Detection at the Edge
Algorithms for radio frequency (RF) spectrum awareness need to be compatible with edge hardware to be practical for many applications. We developed a signal detection and classification model for the ZCU111 RF System-on-a-Chip (RFSoC) that operates on the fast Fourier transform of passband RF data. The system can detect and classify multiple signals of interest and display the predictions in real-time. The model consists of a modified ConvNeXt backbone and YOLOv3 head to operate on the Deep Learning Processing Unit on the RFSoC. We gathered datasets for training and testing by using a software defined radio to transmit example signals of Wi-Fi 802.11 b/g, Wi-Fi 802.11 n, FM Radio, LTE and LTE-M. By leveraging multiple inputs on the RFSoC frontend, the datasets span up to 4 GHz of bandwidth. The models showed high performance in classification accuracy, center frequency error, bandwidth error, and detection accuracy for both single and multi-signal datasets.
42 ENGINEERING↗
COSMIC DAWN: Distributed Analysis of Wireless at Nextscale
Distributed Analysis of Wireless at Nextscale (DAWN) is a novel simulation framework for large-scale design-space exploration (DSE) of unmodified software-defined radio (SDR) applications interacting in a scalable, high-fidelity, virtual physics environment. The software-defined nature of the coupled software-physics simulation leverages hardware emulation to permit in-depth examination and modification of not only the electromagnetic environment, including each signal in flight, but also the precise state of system software and components. DAWN supports modular, customizable physics environments allowing realistic propagation effects so that computationally efficient empirical models, reduced order/surrogate models, or large-scale, high-fidelity, site-specific simulations can be used as a propagation medium based on scenario requirements. This paper introduces DAWN’s design and initial implementation, detailing key architectural components, including the Physics Realization Engine (PhyRE), Runtime Infrastructure for Simulation Environments (RISE), and the design space exploration (DSE) suite. It concludes with demonstrations using unmodified 4G/LTE software available from srsRAN on computing resources ranging from a small cluster to ORNL’s Frontier Exascale system.
Wise, Mike [ORNL] (ORCID:0000000266120641)↗
Real-Time High-Accuracy Digital Wireless Time, Frequency, and Phase Calibration for Coherent Distributed Antenna Arrays
his work presents a fully-digital high-accuracy real-time calibration procedure for frequency and time alignment of open-loop wirelessly coordinated coherent distributed antenna array (CDA) modems, enabling radio frequency (RF) phase coherence of spatially separated commercial off-the-shelf (COTS) software-defined radios (SDRs) without cables or external references such as the global navigation satellite system (GNSS). Building on previous work using high-accuracy spectrally-sparse time of arrival (ToA) waveforms and a multistep ToA refinement process, a high-accuracy two-way time transfer (TWTT)-based time–frequency coordination approach is demonstrated. Due to the two-way nature of the high-accuracy TWTT approach, the time and frequency estimates are Doppler and multipath tolerant, so long as the channel is reciprocal over the synchronization epoch. This technique is experimentally verified using COTS SDRs in a lab environment in static and dynamic scenarios and with significant multipath scatterers. Time, frequency, and phase stability were evaluated by beamforming over coaxial cables to an oscilloscope which achieved time and phase precisions of ~60– 70 ps , with median coherent gains above 99% using optimized coordination parameters, and a beamforming frequency root-mean-square error (RMSE) of 3.73 ppb in a dynamic scenario. Finally, experiments were conducted to compare the performance of this technique with previous works using an analog continuous-wave two-tone (CWTT) frequency reference technique in both static and dynamic settings.
Clock synchronization↗
Radio Frequency Sensor: Very High Frequency Radio Frequency Lightning Detection in Geostationary Orbit
Abstract The Radio Frequency Sensor (RFS), a new radio frequency lightning detector, was launched into geosynchronous orbit in December 2021, and first collected data in January 2022. RFS is a specialized software‐defined radio receiver that detects, records, and reports impulsive broadband radio‐frequency (RF) signatures from lightning in the very high frequency (VHF; 30–300 MHz) range. Its vantage point from a Western hemisphere geosynchronous orbit provides unique opportunities to study evolution of RF lightning signatures over the durations of thunderstorms over the Americas and Pacific Ocean. Its overlapping view with the Geostationary Lightning Mappers (GOES‐16 & 17) enables additional comparisons between the sources of optical emissions and associated VHF emissions that were not possible with previous sensors. We find that RFS preferentially detects bright VHF signals called transionospheric pulse pairs (trans‐ionospheric pulse pairs (TIPPs)). It is estimated that more than 85% of the RFS‐detected lightning events are TIPPs. This paper presents initial results from the first year and a half of on‐orbit operation.
54 ENVIRONMENTAL SCIENCES↗
Accelerated Over-The-Air Neural Receiver Training Using Self-Contrastive Learning
Self-contrastive learning (SCL), a self-supervised learning method, has been shown to improve image and signal classifier accuracies and reduce the training time for neural communications receivers. In particular, prior work has shown that SCL applied as a pre-training step can improve simulated performance of OFDM in 3GPP TDL channel models by reducing the training time of the downstream classification task (demodulation and demapping). In this work a practical implementation demonstrating SCL pre-training using software defined radios (SDRs) is proposed.
Cooke, Corey [ORNL] (ORCID:0000000234263672)↗
Accelerating Next-Generation Cybersecurity R&D Using AI Workflows: BADGER Project Development
The Broadband Automation for Distributed Grid Efficiency and Resilience (BADGER) project aligns with national strategic priorities for integrating emerging wireless technologies and advancing AI-driven security. As critical infrastructure modernizes toward increasingly software-defined and interconnected systems, the ability to leverage 5G/NextG networks and AI-enabled control becomes essential. This report outlines work at the National Laboratory of the Rockies (NLR) to develop a NextG-native security architecture powered by AI-RAN concepts and evaluate workflows that enable efficient and reliable architectures. Together, these efforts position the laboratory to accelerate innovation while directly supporting national security and resilience objectives.
5G/6G↗