# Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels

arXiv: https://arxiv.org/abs/1910.09945 · HTML: https://mlsanity.com/paper/1910.09945/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications](https://mlsanity.com/paper/2207.00300/) (2022)
- [Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance](https://mlsanity.com/paper/2210.16519/) (2022)
- [Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation](https://mlsanity.com/paper/2110.00414/) (2021)
- [Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling](https://mlsanity.com/paper/2110.06787/) (2021)
- [Hybrid Neural Coded Modulation: Design and Training Methods](https://mlsanity.com/paper/2202.01972/) (2022)
- [Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition](https://mlsanity.com/paper/2203.12715/) (2022)
- [Unleash Model Potential: Bootstrapped Meta Self-supervised Learning](https://mlsanity.com/paper/2308.14267/) (2023)
- [Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification](https://mlsanity.com/paper/2501.01620/) (2025)
- [Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification](https://mlsanity.com/paper/2409.12978/) (2024)
- [Team Deep Mixture of Experts for Distributed Power Control](https://mlsanity.com/paper/2007.14147/) (2020)
- [Transfer Bayesian Meta-learning via Weighted Free Energy Minimization](https://mlsanity.com/paper/2106.10711/) (2021)
- [Learn to Adapt to New Environment from Past Experience and Few Pilot](https://mlsanity.com/paper/2209.02649/) (2022)
- [Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization](https://mlsanity.com/paper/2501.01608/) (2025)

## Abstract (arXiv)

When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder. 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. As a result, joint training ideally mimics the operation of non-coherent transmission schemes. In this paper, we propose to obviate the limitations of joint training via meta-learning: Rather than training a common model for all channels, meta-learning finds a common initialization vector that enables fast training on any channel. The approach is validated via numerical results, demonstrating significant training speed-ups, with effective encoders and decoders obtained with as little as one iteration of Stochastic Gradient Descent.