End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning

Abstract

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.

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Cited by in corpus (8)

• Deep Learning Enabled Semantic Communication Systems
• Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning
• AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
• Large-Language-Model Enabled Semantic Communication Systems
• Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance
• A Channel Coding Benchmark for Meta-Learning
• Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels
• Joint Design of Radar Waveform and Detector via End-to-end Learning with Waveform Constraints