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.

翻译：5G蜂窝系统依赖于用户设备与基站之间及时交换反馈控制信息。正确解码该控制信息对于建立和维持高吞吐量无线链路至关重要。本文首次尝试利用机器学习技术提升物理上行控制信道格式0的解码性能。我们采用全连接神经网络，根据接收样本中嵌入的上行控制信息内容对其进行分类。经实时无线捕获数据测试，训练后的神经网络即使在低信噪比条件下，其准确率也较传统的基于DFT的解码器有显著提升。所得准确率结果亦证明其符合3GPP规范要求。