Learning How to Demodulate from Few Pilots via Meta-Learning
Abstract
Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training in order to learn a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data.
Keywords
Cite
@article{arxiv.1903.02184,
title = {Learning How to Demodulate from Few Pilots via Meta-Learning},
author = {Sangwoo Park and Hyeryung Jang and Osvaldo Simeone and Joonhyuk Kang},
journal= {arXiv preprint arXiv:1903.02184},
year = {2021}
}