This paper presents several key contributions towards enabling real-time RF signal analysis using deep learning:
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.
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.
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.
In un'altra lingua
dal contenuto originale
arxiv.org
Approfondimenti chiave tratti da
by Pranav Gokha... alle arxiv.org 04-30-2024
https://arxiv.org/pdf/2404.17962.pdfDomande più approfondite