Neural Network Generative Models for Radio Frequency Data

Joseph Carmack,Amit Bhatia,Josh Robinson,John Majewski,Scott Kuzdeba

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)（2021）

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摘要

Neural Networks (NN) provide great flexibility in modeling non-linear relationships and have recently proved to be very valuable in Radio Frequency (RF) domain, e.g. NN based Digital Signal Processing (DSP) provide equal or better performance than traditional DSP function blocks. One key requirement of NN models is their need for large amounts of training data to mitigate model overfitting. If thi...

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关键词

Radio frequency,RF signals,Training data,Artificial neural networks,Interference,Digital signal processing,Generative adversarial networks

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