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.

翻译：针对未知非线性信道的通信深度学习方法近期引起了广泛关注。本文考虑基于变分推断的半监督学习方法，用于解码未知非线性信道。这些方法包括蒙特卡罗期望最大化算法和变分自编码器，能够高效利用少量导频符号和有效载荷数据。其中，变分自编码器取得了最佳的半监督学习效果。当有效载荷符号足够多时，与利用当前及先前传输块导频数据的元学习方法相比，变分自编码器还可实现更低的误码率。