@inproceedings{9ddf84635bbb476285cd5272e216931c,
title = "Semi-Supervised Variational Inference over Nonlinear Channels",
abstract = "Deep learning methods for communications over unknown nonlinear channels have attracted considerable interest recently. In this paper, we consider semi-supervised learning methods, which are based on variational inference, for decoding unknown nonlinear channels. These methods, which include Monte Carlo expectation maximization and a variational autoencoder, make efficient use of few pilot symbols and the payload data. The best semi-supervised learning results are achieved with a variational autoencoder. For sufficiently many payload symbols, the variational autoencoder also has lower error rate compared to meta learning that uses the pilot data of the present as well as previous transmission blocks.",
keywords = "Channel estimation, semi-supervised learning, variational autoencoders, variational inference",
author = "David Burshtein and Eli Bery",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023 ; Conference date: 25-09-2023 Through 28-09-2023",
year = "2023",
doi = "10.1109/SPAWC53906.2023.10304430",
language = "الإنجليزيّة",
series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "611--615",
booktitle = "2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023 - Proceedings",
address = "الولايات المتّحدة",
}