Narrowband classifier baseline

Purpose

Build a small synthetic dataset for a modulation-classification experiment. Each record contains complex baseband IQ, a structured class label, and the provenance needed to understand how it was generated.

Audience and prerequisites

Use this path for a first classifier-shaped dataset or as a stable baseline for an editable experiment. Install rfgen[torchsig,sionna] (or, from a source checkout, uv pip install -e '.[torchsig,sionna]') because this template selects a TorchSig emitter and the 3GPP TR 38.901 Urban Microcell (UMi) propagation model, an urban-microcell propagation scenario. The Quickstart walks through the same commands and explains the resulting artifacts.

Inputs

The shipped template uses a deterministic seed and four labels: BPSK, QPSK, 16-QAM, and 64-QAM. It is an ordinary YAML file after materialization; it is not a scenario catalog and editing it creates your own experiment. The communication waveforms are built with TorchSig’s constellation-signal primitive; the exact selected class remains stored as rfgen metadata.

What the records carry

Each sample publishes one record: the receiver capture, one declared box per emitter, and a segmentation raster. Label schema states the frames those labels are in — box edges are offsets from the receiver’s tuned centre, and the raster is not a torch.stft grid.

Run

rfgen init narrowband-baseline ./narrowband-config
cd ./narrowband-config
rfgen validate --config-dir .
rfgen generate --config-dir .
rfgen inspect ./rfgen-output

Observable output and interpretation

The template writes 100 records to the local Signal Dataset snapshot rfgen-output under the configuration directory. Every record is a synthetic single-emitter narrowband example whose label is one of the four named modulation classes. The dataset also retains metadata, its RX profile (the receiver-side capture and hardware configuration/effects recorded with the dataset), and Sionna UMi provenance; it is not a directory of unlabeled waveform files.

Verification boundary

The installed-wheel qualification checks the stated record count and label set, store readability, and repeatability for the fixed seed and environment. It does not assert class balance, broad modulation coverage, channel realism, classifier accuracy, or equivalence to over-the-air data.

Customize safely

Copy the materialized configuration to a new directory before changing it. After every edit, run rfgen validate --config-dir DIRECTORY, generate into a new output directory, and record the resulting configuration alongside your training results. For terminology and the data model, read the RF & ML Quick Overview.