@inproceedings{da4f29e0f9c6443d8e1ce1cfa60b32ea,
title = "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 data in order to train 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 = "IoT, MAML, Machine learning, demodulation, meta-learning",
author = "Sangwoo Park and Hyeryung Jang and Osvaldo Simeone and Joonhyuk Kang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019 ; Conference date: 02-07-2019 Through 05-07-2019",
year = "2019",
month = jul,
doi = "10.1109/SPAWC.2019.8815426",
language = "English (US)",
series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019",
address = "United States",
}