  We demonstrate that error correcting codes (ECCs) can be used to construct a
labeled data set for finetuning of "trainable" communication systems without
sacrificing resources for the transmission of known symbols. This enables
adaptive systems, which can be trained on-the-fly to compensate for slow
fluctuations in channel conditions or varying hardware impairments. We examine
the influence of corrupted training data and show that it is crucial to train
based on correct labels. The proposed method can be applied to fully end-to-end
trained communication systems (autoencoders) as well as systems with only some
trainable components. This is exemplified by extending a conventional OFDM
system with a trainable pre-equalizer neural network (NN) that can be optimized
at run time.
