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Updated: Jan 28, 2026

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
Published on: February 3, 2021
A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
Eunmi Chu1, Janghyuk Yoon2, Bang Chul Jung3
1Department of Electronics Engineering, Chungnam National University, Daejeon 34134, Korea. emchu@cnu.ac.kr.
Abstract:
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.
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