  5G cellular systems depend on the timely exchange of feedback control
information between the user equipment and the base station. Proper decoding of
this control information is necessary to set up and sustain high throughput
radio links. This paper makes the first attempt at using Machine Learning
techniques to improve the decoding performance of the Physical Uplink Control
Channel Format 0. We use fully connected neural networks to classify the
received samples based on the uplink control information content embedded
within them. The trained neural network, tested on real-time wireless captures,
shows significant improvement in accuracy over conventional DFT-based decoders,
even at low SNR. The obtained accuracy results also demonstrate conformance
with 3GPP requirements.
