论文标题
5G NR PUCCH格式的机器学习解码器0
Machine Learning Decoder for 5G NR PUCCH Format 0
论文作者
论文摘要
5G蜂窝系统取决于用户设备和基站之间的反馈控制信息的及时交换。对于设置和维持高通量无线电链路，必须正确解码此控制信息。本文首次尝试使用机器学习技术来改善物理上行链路控制通道格式0的解码性能。我们使用完全连接的神经网络根据嵌入其中的上行链路控制信息内容对接收的样本进行分类。经过实时无线捕获测试的训练有素的神经网络，即使在低SNR处，基于DFT的解码器的准确性显着提高。获得的准确性结果还表明了符合3GPP要求的一致性。
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.
