  This paper considers an Internet-of-Things (IoT) scenario in which devices
sporadically transmit short packets with few pilot symbols over a fading
channel. Devices are characterized by unique transmission non-idealities, such
as I/Q imbalance. 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 transmission-side distortion. This paper proposes to tackle
this problem by 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. Various state-of-the-art meta-learning schemes are adapted to the
problem at hand and evaluated, including Model-Agnostic Meta-Learning (MAML),
First-Order MAML (FOMAML), REPTILE, and fast Context Adaptation VIA
meta-learning (CAVIA). Both offline and online solutions are developed. In the
latter case, an integrated online meta-learning and adaptive pilot number
selection scheme is proposed. Numerical results validate the advantages of
meta-learning as compared to training schemes that either do not leverage prior
transmissions or apply a standard joint learning algorithms on previously
received data.
