Evaluation of gNB Monostatic Sensing for UAV Use Case

3GPP Release 19 has initiated the standardization of integrated sensing and communications (ISAC), including a channel model for monostatic sensing, evaluation scenarios, and performance assessment methodologies. These common assumptions provide an important basis for ISAC evaluation, but reproducible end-to-end studies still require a transparent sensing implementation. This paper evaluates 5G New Radio (NR) base station (gNB)-based monostatic sensing for the Unmanned Aerial Vehicle (UAV) use case using a 5G NR downlink Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) waveform and positioning reference signals (PRS), following 3GPP Urban Macro-Aerial Vehicle (UMa-AV) scenario assumptions. We present an end-to-end processing chain for multi-target detection and 3D localization, achieving more than 70% detection probability with less than 5% false alarm rate, in the considered scenario. For correctly detected targets, localization errors are on the order of a few meters, with a 90th-percentile error of 4m and 6m in the vertical and horizontal directions, respectively. To support reproducible baseline studies and further research, we release the simulator 5GNRad, which reproduces our evaluation

Semantically Annotated Multimodal Dataset for RF Interpretation and Prediction

Current limitations in wireless modeling and radio frequency (RF)-based AI are primarily driven by a lack of high-quality, measurement-based datasets that connect RF signals to their physical environments. RF heatmaps, the typical form of such data, are high-dimensional and complex but lack the geometric and semantic context needed for interpretation, constraining the development of supervised machine learning models. To address this bottleneck, we propose a new class of multimodal datasets that combines RF measurements with auxiliary modalities like high-resolution cameras and lidar to bridge the gap between RF signals and their physical causes. The proposed data collection will span diverse indoor and outdoor environments, featuring both static and dynamic scenarios, including human activities ranging from walking to subtle gestures. By achieving precise spatial and temporal co-registration and creating digital replicas for voxel-level annotation, this dataset will enable transformative AI research. Key tasks include the forward problem of predicting RF heatmaps from visual data to revolutionize wireless system design, and the inverse problem of inferring scene semantics from RF signals, creating a new form of RF-based perception.

Detecting Airborne Objects with 5G NR Radars

The integration of sensing capabilities into 5G New Radio (5G NR) networks offers an opportunity to enable the detection of airborne objects without the need for dedicated radars. This paper investigates the feasibility of using standardized Positioning Reference Signals (PRS) to detect UAVs in Urban Micro (UMi) and Urban Macro (UMa) propagation environments. A full 5G NR radar processing chain is implemented, including clutter suppression, angle and range estimation, and 3D position reconstruction. Simulation results show that performance strongly depends on the propagation environment. 5G NR radars exhibit the highest missed detection rate, up to 16%, in UMi, due to severe clutter. Positioning error increases with target distance, resulting in larger errors in UMa scenarios and at higher UAV altitudes. In particular, the system achieves a position error within 4m in the UMi environment and within 8m in UMa. The simulation platform has been released as open-source software to support reproducible research in integrated sensing and communication (ISAC) systems.

Self-Supervised Millimeter Wave Indoor Localization using Tiny Neural Networks

The quasi-optical propagation of millimeter-wave signals enables high-accuracy localization algorithms that employ geometric approaches or machine learning models. However, most algorithms require information on the indoor environment, may entail the collection of large training datasets, or bear an infeasible computational burden for commercial off-the-shelf (COTS) devices. In this work, we propose to use tiny neural networks (NNs) to learn the relationship between angle difference-of-arrival (ADoA) measurements and locations of a receiver in an indoor environment. To relieve training data collection efforts, we resort to a self-supervised approach by bootstrapping the training of our neural network through location estimates obtained from a state-of-the-art localization algorithm. We evaluate our scheme via mmWave measurements from indoor 60-GHz double-directional channel sounding. We process the measurements to yield dominant multipath components, use the corresponding angles to compute ADoA values, and finally obtain location fixes. Results show that the tiny NN achieves sub-meter errors in 74\% of the cases, thus performing as good as or even better than the state-of-the-art algorithm, with significantly lower computational complexity.