  The Tunka Radio Extension (Tunka-Rex) is a digital antenna array operating in
the frequency band of 30-80 MHz, measuring the radio emission of air-showers
induced by ultra-high energy cosmic rays. Tunka-Rex is co-located with the
TAIGA experiment in Siberia and consists of 63 antennas, 57 of them in a
densely instrumented area of about 1km2. The signals from the air showers are
short pulses, which have a duration of tens of nanoseconds and are recorded in
traces of about 5{\mu}s length. The Tunka-Rex analysis of cosmic-ray events is
based on the reconstruction of these signals, in particular, their positions in
the traces and amplitudes. This reconstruction suffers at low signal-to-noise
ratios, i.e. when the recorded traces are dominated by background. To lower the
threshold of the detection and increase the efficiency, we apply advanced
methods of signal reconstruction, namely matched filtering and deep neural
networks with autoencoder architecture. In the present work we show the
comparison between the signal reconstructions obtained with these techniques,
and give an example of the first reconstruction of the Tunka-Rex signals
obtained with a deep neural networks.
