RF Signal Intelligence
Deep learning that adapts to your hardware — high-accuracy signal classification across contested RF environments.
Neural Architecture
What We Do
01
Classifies BPSK, QPSK, 2FSK, and 16QAM signals in real time. Trained on synthetic IQ data spanning a wide SNR range, with no reliance on proprietary datasets or hardware signatures.
02
The model learns the RF characteristics of your front-end and self-optimizes — no manual recalibration. Deploy against USRP, PlutoSDR, RTL-SDR, or recorded IQ captures and the classifier adapts to what it sees.
03
A 3-layer convolutional network with 233K parameters. Designed to run on constrained platforms — no GPU required for inference, suitable for embedded and tactical deployments.
Lab Infrastructure
Models train on synthetic IQ data, then adapt in-situ to the RF characteristics of whatever front-end they're deployed on — learning hardware-specific noise profiles, gain curves, and frequency response without manual recalibration.
The result: a classifier that gets sharper the longer it runs on your hardware, not one that needs to be retrained every time you swap a front-end.
IEEE MILCOM 2026
We are seeking to present and demonstrate our AMR research at IEEE MILCOM 2026. If you are an organizer, program chair, or prospective collaborator, we'd welcome a conversation.
Contact
Whether you're a conference organizer, a defense researcher, or exploring RF intelligence for your program — reach out and we'll get back to you within 48 hours.
hello@faradaylabs.xyz