Augmented Outdoor RSS Dataset for Single- and Two-Transmitter Localization

Augmented Outdoor RSS Dataset for Single- and Two-Transmitter Localization Overview This record contains a physics-informed augmented derivative of the outdoor RSS localization dataset A Dataset of Outdoor RSS Measurements for Localization. The original dataset was collected on the University of Utah / POWDER campus using FRS/GMRS-band radios at 462.7 MHz and includes outdoor RSS measurements for 0-TX, 1-TX, and 2-TX localization scenarios. The present release extends that dataset with synthetic but physically grounded samples generated using PhARMNet: Physics-informed Augmentation and RF Modeling Network. PhARMNet combines terrain-aware propagation features derived from DSM/building maps and TIREM-based modeling with neural RSS prediction to generate additional transmitter-receiver measurements in regions and transmitter configurations that were not exhaustively covered in field collection. This augmented dataset is designed to support: wireless transmitter localization single- and multi-transmitter learning out-of-distribution generalization studies data augmentation for sparse outdoor RSS datasets physics-informed wireless propagation modeling Relationship to the Original Dataset The original dataset provides real-world outdoor RSS measurements collected from the POWDER testbed and includes: Property Original Dataset Total samples 5,214 No-transmitter samples 46 Single-transmitter samples 4,822 Two-transmitter samples 346 Unique transmitter locations 5,514 Receiver count per sample 10–25 Frequency 462.7 MHz Transmit power 1 W This augmented release does not replace the original measured dataset. Instead, it provides additional synthetic samples that are compatible with the original JSON schema and intended to be used alongside the original data. What This Record Adds This release contains two augmented subsets: Augmented Subset Number of Samples Augmented single-transmitter (1-TX) dataset 34,905 Augmented two-transmitter (2-TX) dataset 39,298 These samples were generated from the original POWDER-FRS measurements using PhARMNet-based propagation models. Augmentation Methodology The augmentation pipeline is based on PhARMNet, which combines real measurements with terrain-aware and physics-based propagation features extracted from DSM/building data and TIREM. In the PhARMNet framework, each transmitter-receiver pair is represented using a set of 14 terrain-aware and propagation-aware features, including line-of-sight / non-line-of-sight indicators, diffraction-related quantities, elevation angles, obstacle counts, knife-edge effects, and shadowing geometry. Single-transmitter augmentation For the 1-TX case, grouped receiver-specific measurements from the original single-transmitter dataset were used to train receiver-type-aware PhARMNet RSS predictors. These trained models were then evaluated over precomputed transmitter coordinate libraries in order to synthesize new RSS values for additional transmitter locations. Two-transmitter augmentation For the 2-TX case, a two-input variant of the PhARMNet propagation model was used. New transmitter-pair configurations were formed from available single-transmitter locations, and trained models were used to predict RSS measurements for these synthetic transmitter pairs. This makes it possible to enlarge the 2-TX dataset substantially beyond the small number of directly measured two-transmitter samples available in the original record. Why augmentation is needed The original outdoor dataset is valuable but sparse, especially in the multi-transmitter setting, where the number of measured 2-TX samples is limited. The PhARMNet paper specifically motivates augmentation as a way to improve spatial coverage and support better localization performance in sparse and out-of-distribution regions. File Format The augmented files follow the same general JSON organization as the original dataset family. Each top-level entry is indexed by a timestamp-like key and contains: Field Description rx_data List of receiver-side RSS measurements with receiver GPS coordinates and receiver names tx_coords GPS coordinates of the active transmitter(s) metadata Per-transmitter metadata, compatible with the original schema Sample structure { "2022-04-25 14:11:02": { "rx_data": [ [-75.14, 40.76, -111.85, "receiver-name"] ], "tx_coords": [ [40.767, -111.846] ], "metadata": [ {"power": 1, "transport": "augmented", "radio": "TXA"} ] }} For the augmented 2-TX case, tx_coords contains two transmitter coordinates and metadata contains two corresponding transmitter entries. Important Notes Synthetic values: RSS values in this record are model-generated, not directly measured in the field. Schema compatibility: The JSON structure is intentionally designed to remain compatible with the original dataset format. Timestamp fields: Timestamp keys are synthetic unique identifiers created for schema consistency; they should not be interpreted as original collection times. Coordinate format: Final exported coordinates are given in GPS latitude/longitude, consistent with the original dataset representation. RSS units: Final exported RSS values are provided in dB, after converting model outputs back from normalized training-space values. Intended Use This dataset may be useful for: training and evaluation of RSS-based localization models studying sparse-data and out-of-distribution localization comparing measured and synthetic propagation data testing single- vs. multi-transmitter localization pipelines physics-informed augmentation for wireless networking tasks Because this is an augmented derivative dataset, users are encouraged to use it together with the original measured data and to clearly distinguish between measured and synthetic samples in downstream experiments. Provenance and References This augmented dataset is derived from: Original dataset:Frost Mitchell, Aniqua Baset, Sneha Kumar Kasera, and Aditya Bhaskara.A Dataset of Outdoor RSS Measurements for Localization.Zenodo. DOI: 10.5281/zenodo.7259895 Augmentation methodology:Md Mumtahin Habib Ullah Mazumder, Frost Mitchell, Aditya Bhaskara, Sneha Kumar Kasera, and Neal Patwari.Bridging Data Gaps: Enhancing Wireless Localization with Physics-Informed Data Augmentation.Proceedings of the ACM on Networking, 2025. DOI: 10.1145/3768995 Suggested Citation for This Record If you use this augmented dataset, please cite: this Zenodo record, the original POWDER-FRS dataset, and the PhARMNet paper.