标题： 基于WGAN的空中自动编码器训练 显示英文标题

标题： WGAN-based Autoencoder Training Over-the-air

摘要： 端到端通信系统训练的实际实现从根本上受到信道梯度可访问性的限制。 为了克服这一主要负担，文献中最近提出了生成对抗网络（GANs）的想法，该网络学习模仿实际的信道行为。 与无法完全捕捉现实世界的传统手工设计信道建模相反，理论上，GANs能够通过数据驱动的学习算法学习任何物理损伤。 在本工作中，我们验证了基于GAN的自动编码器训练在实际空中（OTA）测量中的概念。 为了提高训练稳定性，我们首先将这一概念扩展到条件Wasserstein GANs，并将其嵌入到最先进的自动编码器架构中，包括位级估计和外部信道码。 此外，在同一框架下，我们比较了现有的三种不同的训练方法：基于模型的预训练结合接收机微调、强化学习（RL）和基于GAN的信道建模。 为此，我们展示了基于GAN的端到端训练的优势和局限性。 特别是，对于非线性效应，发现学习整个探索空间变得极其复杂。 最后，我们表明，与需要连续发射机权重更新的基于RL的训练相比，训练策略在数据获取方面受益于更简单的（训练）数据获取。 由于发射机和训练算法之间的带宽和延迟有限，甚至可能在物理上位于不同位置，这成为一个重要的实际瓶颈。 显示英文摘要

摘要： The practical realization of end-to-end training of communication systems is fundamentally limited by its accessibility of the channel gradient. To overcome this major burden, the idea of generative adversarial networks (GANs) that learn to mimic the actual channel behavior has been recently proposed in the literature. Contrarily to handcrafted classical channel modeling, which can never fully capture the real world, GANs promise, in principle, the ability to learn any physical impairment, enabled by the data-driven learning algorithm. In this work, we verify the concept of GAN-based autoencoder training in actual over-the-air (OTA) measurements. To improve training stability, we first extend the concept to conditional Wasserstein GANs and embed it into a state-of-the-art autoencoder-architecture, including bitwise estimates and an outer channel code. Further, in the same framework, we compare the existing three different training approaches: model-based pre-training with receiver finetuning, reinforcement learning (RL) and GAN-based channel modeling. For this, we show advantages and limitations of GAN-based end-to-end training. In particular, for non-linear effects, it turns out that learning the whole exploration space becomes prohibitively complex. Finally, we show that the training strategy benefits from a simpler (training) data acquisition when compared to RL-based training, which requires continuous transmitter weight updates. This becomes an important practical bottleneck due to limited bandwidth and latency between transmitter and training algorithm that may even operate at physically different locations.