Department of Electronics Engineering, Chungnam National University, Daejeon 34134, Korea.
Sensors (Basel). 2019 Mar 8;19(5):1196. doi: 10.3390/s19051196.
In this paper, we propose a novel machine learning (ML) based link-to-system (L2S) mapping technique for inter-connecting a link-level simulator (LLS) and a system-level simulator (SLS). For validating the proposed technique, we utilized 5G K-Simulator, which was developed through a collaborative research project in Republic of Korea and includes LLS, SLS, and network-level simulator (NS). We first describe a general procedure of the L2S mapping methodology for 5G new radio (NR) systems, and then, we explain the proposed ML-based exponential effective signal-to-noise ratio (SNR) mapping (EESM) method with a deep neural network (DNN) regression algorithm. We compared the proposed ML-based EESM method with the conventional L2S mapping method. Through extensive simulation results, we show that the proposed ML-based L2S mapping technique yielded better prediction accuracy in regards to block error rate (BLER) while reducing the processing time.
在本文中，我们提出了一种新的基于机器学习（ML）的链路到系统（L2S）映射技术，用于连接链路级模拟器（LLS）和系统级模拟器（SLS）。为了验证所提出的技术，我们使用了韩国合作研究项目开发的 5G K-Simulator，它包括 LLS、SLS 和网络级模拟器（NS）。我们首先描述了 5G 新无线电（NR）系统的 L2S 映射方法的一般过程，然后，我们用深度神经网络（DNN）回归算法解释了所提出的基于 ML 的指数有效信噪比（SNR）映射（EESM）方法。我们将基于 ML 的 EESM 方法与传统的 L2S 映射方法进行了比较。通过广泛的仿真结果，我们表明，所提出的基于 ML 的 L2S 映射技术在降低处理时间的同时，在误块率（BLER）方面具有更好的预测精度。
