Papers for

spectrum monitoring operators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

FlowSep-RF separates overlapping radio signals with one-step method

FlowSep-RF: Rectified Flow Matching for Single-Channel Radio-Frequency Signal Separation

Abstract: Single-channel radio-frequency (RF) signal separation is important for interference mitigation when a signal of interest overlaps with a co-channel interferer in time and frequency. Classical matched filtering and LMMSE receivers are limited under structured non-Gaussian interference, while diffusion and score-based methods often require iterative sampling or optimization. We propose FlowSep-RF, a rectified flow matching framework that learns a clean-endpoint transport from the received mixture to the signal of interest. The model combines time-domain IQ samples with frequency-domain features and recovers the separated waveform using one endpoint velocity evaluation. We evaluate FlowSep-RF on QPSK mixtures with OFDM-BPSK, OFDM-QPSK, and RFChallenge CommSignal2 interference against classical, discriminative, and generative baselines. FlowSep-RF achieves the lowest average BER and MSE among evaluated baselines across all three settings with one-step inference.

Wed 14 OctMachine Learning

The gist

When two radio signals overlap in time and frequency, it can be hard to hear the signal you want without interference. The authors designed FlowSep-RF, a method that learns to separate these signals by transforming the mixed radio data into the clean signal with just one step. Their approach combines time and frequency information, making it simpler and more accurate than older techniques. Tests showed FlowSep-RF reduces errors better than other methods for several types of radio signal mixtures.

Open → 2610.20269v1