  When a channel model is not available, the end-to-end training of encoder and
decoder on a fading noisy channel generally requires the repeated use of the
channel and of a feedback link. An important limitation of the approach is that
training should be generally carried out from scratch for each new channel. To
cope with this problem, prior works considered joint training over multiple
channels with the aim of finding a single pair of encoder and decoder that
works well on a class of channels. In this paper, we propose to obviate the
limitations of joint training via meta-learning. The proposed approach is based
on a meta-training phase in which the online gradient-based meta-learning of
the decoder is coupled with the joint training of the encoder via the
transmission of pilots and the use of a feedback link. Accounting for channel
variations during the meta-training phase, this work demonstrates the
advantages of meta-learning in terms of number of pilots as compared to
conventional methods when the feedback link is only available for meta-training
and not at run time.
