  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.
