Concepts de base
Deep learning models can enable accurate and low-latency classification of radio frequency (RF) signals, including those from emerging quantum RF (QRF) sensors based on Rydberg atoms.
Résumé
This paper presents several key contributions towards enabling real-time RF signal analysis using deep learning:
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Development of a Continuous Wavelet Transform (CWT) based Recurrent Neural Network (RNN) model that can perform online classification of RF signals with minimal sampling time. This CWT-RNN approach achieves high classification accuracy for both modulation and signal-to-noise ratio (SNR) tasks, while enabling rapid decision-making from just a fraction of the input signal.
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Extensive latency optimizations for deep learning inference, spanning both GPU and CPU implementations. Through techniques like mixed-precision quantization, the authors achieve over 100x reductions in inference time compared to a baseline, enabling sub-millisecond latency that is suitable for real-time RF processing.
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Validation of the deep learning models on simulated data from emerging Quantum RF (QRF) sensors based on Rydberg atoms. The authors demonstrate that their CWT-RNN approach can effectively classify QRF sensor outputs, paving the way for integrating advanced AI/ML techniques with next-generation quantum-based RF hardware.
Overall, this work bridges the gap between powerful deep learning methods and the stringent latency requirements of real-world RF sensing applications, while also demonstrating the portability of these techniques to novel quantum-based RF sensing platforms.
Stats
"Recent work has shown the promise of applying deep learning to enhance software processing of radio frequency (RF) signals."
"Traditionally, RF sensors have relied on conventional signal processing techniques that leverage predetermined algorithms and hard-coded system responses."
"AI/ML systems for signal processing offer the potential to overcome these prior limitations."
"We find that the 9-SNR classification task achieves 70% accuracy on the validation set, with higher performance in the high-SNR regime."
"The CWT-RNN exhibits remarkably high accuracy from the first input, with the maximum accuracy achieved at only a fraction of the signal length."
"We find that the FP16 mixed precision GPU implementation had inference time per batch of 2.831(2) ms, which is 2.3×faster than the warm start."
"The float16 dynamically quantized CPU model achieves inference times of 0.65(3) ms for batch size 1."
"Remarkably, the CWT-RNN approach exhibits improved performance on the QRF dataset, with both the 5-SNR and 9-SNR classification tasks obtaining top-1 classification accuracies above 98%, with > 70% accuracy from the first timestep."
Citations
"Recent work has shown the promise of applying deep learning to enhance software processing of radio frequency (RF) signals."
"AI/ML systems for signal processing offer the potential to overcome these prior limitations."
"The CWT-RNN exhibits remarkably high accuracy from the first input, with the maximum accuracy achieved at only a fraction of the signal length."