Machine Learning Decoder for 5G NR PUCCH Format 0

TL;DR

This paper introduces a machine learning-based decoder for 5G NR PUCCH Format 0, demonstrating improved accuracy over traditional methods, especially in low SNR conditions, and meeting industry standards.

Contribution

It is the first to apply neural networks for decoding 5G PUCCH Format 0, enhancing performance beyond conventional DFT-based decoders.

Findings

Neural network decoder outperforms traditional decoders in accuracy.

Decoding accuracy remains high even at low SNR levels.

Results conform to 3GPP requirements.

Abstract

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.