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arXiv · 2603.09201

The Radio-Frequency Transformer for Signal Separation

Egor Lifar·Semyon Savkin·Rachana Madhukara·Tejas Jayashankar·Yury Polyanskiy·Gregory W. Wornell
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Abstract

We study a problem of signal separation: estimating a signal of interest (SOI) contaminated by an unknown non-Gaussian background/interference. Given the training data consisting of examples of SOI and interference, we show how to build a fully data-driven signal separator. To that end we learn a good discrete tokenizer for SOI and then train an end-to-end transformer on a cross-entropy loss. Training with a cross-entropy shows substantial improvements over the conventional mean-squared error (MSE). Our tokenizer is a modification of Google's SoundStream, which incorporates additional transformer layers and switches from VQVAE to finite-scalar quantization (FSQ). Across real and synthetic mixtures from the MIT RF Challenge dataset, our method achieves competitive performance, including a 122x reduction in bit-error rate (BER) over prior state-of-the-art techniques for separating a QPSK signal from 5G interference. The learned representation adapts to the interference type without side information and shows zero-shot generalization to unseen mixtures at inference time, underscoring its potential beyond RF. Although we instantiate our approach on radio-frequency mixtures, we expect the same architecture to apply to gravitational-wave data (e.g., LIGO strain) and other scientific sensing problems that require data-driven modeling of background and noise.

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Egor Lifar, Semyon Savkin, Rachana Madhukara, Tejas Jayashankar, Yury Polyanskiy, Gregory W. Wornell. 2026-03-10. The Radio-Frequency Transformer for Signal Separation. https://arxiv.org/abs/2603.09201

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