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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.

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Summary

This study introduces a machine learning (ML) based link-to-system (L2S) mapping technique for 5G New Radio (NR) systems. The novel ML-based exponential effective signal-to-noise ratio (SNR) mapping (EESM) method improves prediction accuracy and reduces processing time.

Keywords:
deep neural network (DNN)exponential effective SNR mapping (EESM)link-to-system mappingmachine learningphysical-layer abstractionsystem-level simulation

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Area of Science:

  • Telecommunications Engineering
  • Computer Science
  • Machine Learning

Background:

  • Accurate simulation of wireless systems requires effective inter-connection between link-level simulators (LLS) and system-level simulators (SLS).
  • Existing link-to-system (L2S) mapping methods may lack prediction accuracy or efficiency for complex 5G New Radio (NR) systems.

Purpose of the Study:

  • To propose and validate a novel machine learning (ML) based link-to-system (L2S) mapping technique for 5G NR systems.
  • To enhance the prediction accuracy and reduce the processing time of L2S mapping.

Main Methods:

  • Developed a machine learning (ML) based exponential effective signal-to-noise ratio (SNR) mapping (EESM) method utilizing a deep neural network (DNN) regression algorithm.
  • Integrated and validated the proposed technique within the 5G K-Simulator, which includes LLS, SLS, and network-level simulator (NS).
  • Compared the ML-based EESM method against conventional L2S mapping techniques.

Main Results:

  • The proposed ML-based L2S mapping technique demonstrated superior prediction accuracy concerning block error rate (BLER).
  • Significant reduction in processing time was observed compared to conventional L2S mapping methods.
  • The ML-based EESM method effectively bridges the gap between LLS and SLS for 5G NR simulations.

Conclusions:

  • The novel ML-based EESM technique offers a more accurate and efficient approach for L2S mapping in 5G NR systems.
  • This method can significantly improve the performance of system-level simulations by leveraging ML for accurate link-level predictions.
  • The findings contribute to advancing simulation methodologies in wireless communication research.