论文标题
基于深神经网络的OFDM系统的命理学选择
Numerology Selection for OFDM Systems Based on Deep Neural Networks
论文作者
论文摘要
为了支持各种场景和部署，定义了子载体间距和循环前缀（CP）的参数化正交频施加多路复用（OFDM）的命理学。移动无线电通道的时频分散和通道噪声导致不同的命理性的性能恶化。在这封信中，我们提出了一种深度中性网络（DNN）方法，用于选择OFDM系统的命理学。考虑到符号间干扰（ISI），载体间干扰（ICI）和噪声水平，将SNR丢失确定为最小化的目标。我们提取功率延迟轮廓，移动速度和噪声功率作为DNN的输入功能。提出的DNN从信道特征中学习，以获得最佳命理选择。仿真结果表明，所提出的DNN比现有方法更好的性能。还说明了不同命理的决策边界，以根据渠道特征显示应用范围。
In order to support diverse scenarios and deployments, the numerology of orthogonal frequency division multiplexing (OFDM) is defined for the parametrization of subcarrier spacing and cyclic prefix (CP). The time-frequency dispersion of mobile radio channels and the channel noise result in different performance deterioration in different numerologies. In this letter, we propose a deep neutral network (DNN) approach for numerology selection of OFDM systems. Considering the inter-symbol interference (ISI), inter-carrier interference (ICI) and noise level, the SNR loss is established as the objective to be minimized. We extract the power delay profile, mobile velocity and noise power as the input features to the DNN. The proposed DNN learns from the channel characteristics to obtain the optimal numerology selection. Simulation results show that the proposed DNN achieves better performance than the existing methods. The decision boundaries of different numerologies are also illustrated to show the application range according to the channel characteristics.
