EMMA-FP v0.1¶
Warning
Pre-implementation. This page describes proposed contracts. Class signatures, parameter types, schema fields, and behavior are subject to change before code lands. Once implementation exists, content here will be regenerated from docstrings or sourced from running tests.
Dataset. EMMA-FP-v0.1 covers the task, target domain, input, labels, splits, and the download command; this recipe pins only the rfgen configuration. Source. rfgen (primary).
rfgen recipe¶
rfgen commit:
<rfgen-commit>(frozen at release; placeholder until pinned).Emitters: the rfgen
emitter_zoogroup,commsfamily, with multiple device instances per model carrying per-device fingerprint priors. The rfgenFingerprintConfigpins CFO (carrier frequency offset), IQ imbalance, and phase noise per unit (cfo_hz_range,iq_imbalance_db_range,phase_noise_dbc_hz_range).Receiver array: 8-element ULA (uniform linear array), half-wavelength spacing (
spacing_lambda: 0.5), via the rfgenMultiRXConfig.geometrymemberULA. Loaded into ArraySpec at load time.Channel environment(s):
E_URBANandE_RURAL, pinned through the rfgenchannelgroup.Labeler + label extraction: the rfgen
labelgroup records the realized device instance; LabelExtractor mapsemitters[].device_idto the device-identity label.SNR (signal-to-noise ratio) regime: drawn from the rfgen
channel.snr_db_range.Sample counts: approximately 60k scenes total.
Splits + seeds:
train/dev/holdoutseed ranges pinned at release; the holdout is a frozen secret seed, never published. OOD axis:LEAVE_ONE_UNIT_OUT(every capture of one device held out together, so device identity is isolated from capture session).
Real-capture counterpart¶
EMMA-REAL-OOD-v0.1. Real-capture device fingerprints are not wired at v0.1 (synthetic only).
Notes¶
The confound fix: existing RF fingerprinting sets confound device identity with capture session, because each device is recorded in one session on one receiver. EMMA’s leave-one-unit-out protocol and per-device
FingerprintConfigrandomization are designed to isolate device identity. The recipe must also randomize the receiver front-end across units before scoring, or the score measures the receiver chain rather than the device.Construct validity is entirely conditional on a rigorous leave-one-unit-out recipe; see Datasets validation.
References¶
DARPA RFMLS program, 2017 to 2021. The capability-without-public-benchmark precedent for fingerprinting. (verify program record)
Bihl, Bauer, and Temple, “Feature representation for RF fingerprinting,” 2017. SEI literature. (verify)
“No Radio Left Behind,” 2019. Cross-session device identification; the leave-one-unit-out motivation. (verify)
See Also¶
Task reference: the
E-ID-FPcontract.Datasets: the dataset registry.
Dataset recipe: the recipe schema this page pins.
Datasets validation: the session-confound and receiver-chain audit.