  Radio detection of air showers produced by ultra-high energy cosmic rays is a
cost-effective technique for the next generation of sparse arrays. The
performance of this technique strongly depends on the environmental background,
which has different constituents, namely anthropogenic radio frequency
interference, synchrotron galactic radiation and others. These components have
recognizable features, which can help for background suppression. A powerful
method for handling this is the application of convolution neural networks with
a specific architecture called autoencoder. By suppressing unwanted signatures,
the autoencoder keeps the signal-like ones. We have successfully developed and
trained an autoencoder, which is now applied to the data from Tunka-Rex. We
show the procedures of the training and optimization of the network including
benchmarks of different architectures. Using the autoencoder, we improved the
standard analysis of Tunka-Rex in order to lower the threshold of the
detection. This enables the reconstructing of sub-threshold events with
energies lower than 0.1 EeV with satisfactory angular and energy resolutions.
