The problem of end-to-end learning of a communication system using an autoencoder -- consisting of an encoder, channel, and decoder modeled using neural networks -- has recently been shown to be an effective approach. A challenge faced in the practical adoption of this learning approach is that under changing channel conditions (e.g. a wireless link), it requires frequent retraining of the autoencoder in order to maintain a low decoding error rate. Since retraining is both time consuming and requires a large number of samples, it becomes impractical when the channel distribution is changing quickly. We propose to address this problem using a fast and sample-efficient (few-shot) domain adaptation method that does not change the encoder and decoder networks. Different from conventional training-time unsupervised or semi-supervised domain adaptation, here we have a trained autoencoder from a source distribution that we want to adapt (at test time) to a target distribution using only a small labeled dataset, and no unlabeled data. We focus on a generative channel model based on the Gaussian mixture density network (MDN), and propose a regularized, parameter-efficient adaptation of the MDN using a set of affine transformations. The learned affine transformations are then used to design an optimal transformation at the decoder input to compensate for the distribution shift, and effectively present to the decoder inputs close to the source distribution. Experiments on many simulated distribution changes common to the wireless setting, and a real mmWave FPGA testbed demonstrate the effectiveness of our method at adaptation using very few target domain samples. The code for our work can be found at: https://github.com/jayaram-r/domain-adaptation-autoencoder.

翻译：使用自编码器（由神经网络建模的编码器、信道和解码器组成）进行通信系统的端到端学习，已被证明是一种有效方法。然而，在实际应用中，该方法面临一个挑战：当信道条件（如无线链路）发生变化时，需要频繁重新训练自编码器以维持较低的解码错误率。由于重新训练既耗时又需要大量样本，当信道分布快速变化时，该方法变得不切实际。我们提出采用一种快速且样本高效（小样本）的域自适应方法来解决此问题，该方法无需修改编码器和解码器网络。与传统的训练时无监督或半监督域自适应不同，本文的场景是：我们将源分布中训练好的自编码器，在测试时仅利用少量标注数据集（无未标注数据）自适应到目标分布。我们聚焦于基于高斯混合密度网络（MDN）的生成信道模型，并提出一种正则化、参数高效的MDN自适应方法，该方法使用一组仿射变换。随后利用学习到的仿射变换，在解码器输入端设计最优变换，以补偿分布偏移，使解码器输入有效逼近源分布。我们在无线场景中常见的多种模拟分布变化及真实毫米波FPGA测试平台上进行实验，结果表明，本方法仅需极少量目标域样本即可实现高效自适应。本工作代码见：https://github.com/jayaram-r/domain-adaptation-autoencoder。