# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
## 1 Introduction  
With technological advance of 4G LTE/LTE-A (Long Term Evolution-Advanced) cellular communications, the number of wireless smart devices has increased explosively. Wireless smart devices have being applied in various services such as IoT (Internet of Things) communications, autonomous vehicles communication, augmented reality service, etc. [1]. In the near future, a wide range of service requirements will be demanded due to advent of many use cases [2]. However, 4G LTE/LTE-A systems have several limitations to satisfy requirements of various services. As the next version of 4G LTE/LTE-A, 3 GPP Release 15 (Rel-15) has specified 5G new radio access (NR) and core technologies from December 2017 and it is known as 5G NR. 5G NR access technology is evolving to support a variety of service requirements and it contains enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra reliable low latency communications services (URLLC) as 5G representative services [3]. To support low latency service, 5G NR system has embraced new flexible frame structures different from 4G LTE/LTE-A system with a fixed sub-carrier interval. Moreover, to support high data rate and high traffic service for eMBB use cases, 5G NR systems are expanding to high frequency bands above 6 GHz. Accordingly, 5G NR system will be mainly operated on high frequency band with a wide bandwidth [4]. In a wide band channel, a transport block (TB) is allocated into \(N\) narrow band channels and each narrow band channel goes through a different fading condition on its own sub-carrier. Therefore, user equipment (UE) experiences different post-processing signal to interference plus noiseratio (SINR) over every sub-carrier. In a traditional narrow band channel, block error rate (BLER) is estimated from a curve of mean SINR and mean BER. On the contrary, in the wide band channel, different \(N\) post-processing SINRs are mapped to the averaged post-processing SINR. Since the concept of the averaged post-processing SINR is defined as an effective SNR, this many-to-one mapping is called an effective SNR mapping (ESM) technique. Besides, ESM technique is used for the purpose of physical layer abstraction when evaluating a system-level simulator (SLS). A simplified link-level simulator (LLS) helps SLS reduce complexity of computation and it can help improve a simulator performance. Since the concept of a physical level abstraction for SLS is reflected, this is also called a link-to-system (L2S) mapping technique. Accordingly, a L2S mapping is that post-processing SINRs extracted from LLS are mapped to an effective SNR and BLER is predicted by the effective SNR, thus, it is called effective SNR mapping (ESM). In prior studies, many researchers analyzed exponential effective SINR mapping (ESM) [5; 6] and mutual information based ESM [7] as representative L2S mappings. In [8], effective SNR is analyzed on the side of uplink. In [9], impact of L2S is analyzed on the side of system level. However, there are too many data extracted from LLS as well as too much processing time is need to find EESM mapping parameters for various cases. Moreover, loss incurs due to an inaccuracy from additive white Gaussian noise (AWGN) curve of SNR and BLER. Therefore, recently, researchers studied ML-based link abstraction models. In [10], support vector machine is used to enable ML classification for fast adaptive modulation coding. This scheme exploits measurement of single TB success or failure to train the classifier. In [11], a ML method based on a logistic regression is proposed. To predict a TB success or failure, their basic model uses mean and standard deviation of the SINR set, modulation rate, and TB size as input variables. To improve the estimation accuracy, adding terms of higher order or combinations of input variables are used in an enhanced model. To utilize ML-based link abstraction models that have been studied thus far, ML algorithms should be applied on both of an evolved Node-B (eNB) and UE sides.