aiqbench analysis
FlowSep-RF applies rectified flow matching, a generative AI technique, to solve single-channel RF signal separation, directly addressing interference mitigation in wireless communications. By combining time-domain IQ samples with frequency-domain features, the method achieves superior bit error rate and mean squared error performance compared to classical receivers and other generative baselines using only one-step inference. This work represents a significant advancement in applying machine learning models to structured non-Gaussian interference scenarios where traditional filtering methods fail.
FlowSep-RF: Rectified Flow Matching for Single-Channel Radio-Frequency Signal Separation
arXiv:2610.20269v1 Announce Type: cross 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.
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.
5 pages. Preprint. Under review
Signal Processing (eess.SP); Machine Learning (cs.LG)
arXiv:2610.20269 [eess.SP]
(or arXiv:2610.20269v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2610.20269 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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