  Due to the rapid development of autonomous driving, the Internet of Things
and streaming services, modern communication systems have to cope with varying
channel conditions and a steadily rising number of users and devices. This, and
the still rising bandwidth demands, can only be met by intelligent network
automation, which requires highly flexible and blind transceiver algorithms. To
tackle those challenges, we propose a novel adaptive equalization scheme, which
exploits the prosperous advances in deep learning by training an equalizer with
an adversarial network. The learning is only based on the statistics of the
transmit signal, so it is blind regarding the actual transmit symbols and
agnostic to the channel model. The proposed approach is independent of the
equalizer topology and enables the application of powerful neural network based
equalizers. In this work, we prove this concept in simulations of different --
both linear and nonlinear -- transmission channels and demonstrate the
capability of the proposed blind learning scheme to approach the performance of
non-blind equalizers. Furthermore, we provide a theoretical perspective and
highlight the challenges of the approach.
