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arXiv Machine Learning By Rahul Jain, Pierre Trepagnier, Rick Gentile, Joey Botero, Alexia Schulz

Clearing the Underbrush: AI-Enhanced RF Interference Suppression

Read the original on arXiv Machine Learning →

The paper presents an AI‑enhanced method for radio frequency interference suppression that builds on autoregressive transformer models by adding a Finite Scalar Quantization tokenizer layer. This addition improves interference rejection while maintaining low latency, and the authors also test other inference optimizations to speed up processing with minimal accuracy loss. Experiments using a digitally modulated RF signal as the signal of interest and a digital television OFDM signal as interference show that the approach outperforms traditional techniques and prior AI methods, with benefits demonstrated through audio quality metrics like PESQ and potential operational applications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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