End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning
This addresses the challenge of fast adaptation in communication systems for scenarios with limited feedback, though it is incremental as it builds on prior joint training methods.
The paper tackles the problem of end-to-end training of encoder and decoder for communication links without a channel model, which typically requires retraining for each new channel, by proposing an online meta-learning approach that reduces the number of pilots needed compared to conventional methods when feedback is only available during meta-training.
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.