# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
## 1 Introduction
However, since the number of UEs is too large, it is difficult to embed ML algorithms in all UEs. Some UEs can directly apply ML algorithms while other UEs should take the existing EESM method. Therefore, eNBs still need the existing EESM method. In our previous work [12], we proposed a ML-based EESM method where training data are learned by deep neural network (DNN) regression and L2S mapping based on EESM is executed by optimization algorithms under 4G LTE system environments. We showed that the processing time and accuracy are improved. However, L2S mapping-related previous studies including our previous work have been done based on 3G/4G cellular networks. Our previous work is meaningful as the existing results under SISO condition of 4G LTE systems are applied to machine learning techniques. Recently, the standards of 5G NR systems based on Rel-15 decided that LDPC channel coding on data plane is preferred and clustered delay line (CDL) and tapped delay line (TDL) as 5G channel models are used. Since the standards of 5G NR based on Rel-15 are continually progressing, the performance results for AWGN channel of simple model are not easy to obtain. To develop LLS, SLS, and network-level simulator (NS) for 5G NR based on Rel-15, a Korean collaborative research project developed 5G K-Simulator during August 2016-February 2019 [13; 14]. Our role was to develop a L2S mapping method as an inter-connection between the LLS and the SLS in 5G K-Simulator. To the best of our knowledge, were were the first to design a machine learning based L2S mapping under 5G NR systems based on Rel-15. The main contributions of this paper are as follows:  
1. AWGN curves based on 5G NR system are provided. In conventional L2S mapping schemes, an AWGN curve is required to estimate block error rate (BLER) on SLS. We provide the AWGN curve of SNR-BLER under 5G NR systems and compare the AWGN curves of SNR-BLER between 4G LTE system and 5G NR systems. 2. The values of SNR satisfying at a target BLER = 0.1 (10%) are provided through AWGN curve. 3 GPP 4G LTE and 5G NR-standards are specified to set the target BLER of 0.1, i.e., 10%. In SLS, signal-to noise ratio (SNR) can be measured immediately, but the calculation of BLER is burden because CRC decoding has to be performed. When a measured BLER is lower than the target BLER, UE can change to the index of higher CQI to support high data rate. Therefore, we provide the values of SNR thresholds under an unknown BLER satisfying the target BLER of 0.1 through the simulation results. 3. The methodology for 5G NR L2S mapping is provided based on ML. The optimal parameters for L2S mapping depend on a given environment. In addition, 5G NR systems have flexible frame structures. Therefore, we provide the methodology for 5G NR L2S mapping based on ML to extract the data for any cases and find the optimal mapping parameters. 4. The optimal mapping parameters for L2S mapping are provided in SISO and 2 \(\times\) 2 MIMO cases under given conditions. This paper is organized as follows. In Section 2, we introduce 5G NR system and main difference of 4G and 5G systems. In Section 3, we describe the methodology for 5G NR L2S mapping. In Section 4, we propose machine learning-based effective SNR mapping procedure. In Section 5, we validate the proposed scheme and show the simulation results of the proposed scheme. Finally, we draw conclusions in Section 6. The abbreviations commonly used in this paper are summarized as Table 1.