  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.
