A Novel Machine Learning Approach for Link Adaptation in 5G Wireless Networks

This study addresses a Machine Learning (ML) based Link Adaptation (LA) scheme for 5G New Radio (NR) wireless networks, which aims to improve the system throughput by selecting the best possible choice of the Modulation Coding Scheme (MCS). This work proposes a Deep Neural Network(DNN) based regress...

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Veröffentlicht in:2020 2nd PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS) S. 1 - 2
Hauptverfasser: Pati, Preeti Samhita, Sahoo, Shubham Somnath, Krishnaswamy, Dilip, Datta, Raja
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 08.11.2020
Schlagworte:
Adaptation models
Deep Neural Network (DNN)
Interference
Link Adaptation (LA)
Mathematical model
Modulation
Modulation Coding Scheme (MCS)
Signal to noise ratio
Training data
Wireless networks
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Abstract This study addresses a Machine Learning (ML) based Link Adaptation (LA) scheme for 5G New Radio (NR) wireless networks, which aims to improve the system throughput by selecting the best possible choice of the Modulation Coding Scheme (MCS). This work proposes a Deep Neural Network(DNN) based regression model to maximize the Spectral Efficiency (SE) of the system under the 10% Block Error Rate (BLER) and thus finding the best MCS. We consider a 5G NR Frequency Range-1(FR-1), i.e., the Sub-6GHz operating band for the study. Our simulation results show the mapping of Signal to Interference and Noise Ratio (SINR) to the Channel Quality Indicator (CQI) and thus the best possible selection of modulation and coding scheme in case of perfect channel estimation based system which is found to improve the system throughput.
AbstractList This study addresses a Machine Learning (ML) based Link Adaptation (LA) scheme for 5G New Radio (NR) wireless networks, which aims to improve the system throughput by selecting the best possible choice of the Modulation Coding Scheme (MCS). This work proposes a Deep Neural Network(DNN) based regression model to maximize the Spectral Efficiency (SE) of the system under the 10% Block Error Rate (BLER) and thus finding the best MCS. We consider a 5G NR Frequency Range-1(FR-1), i.e., the Sub-6GHz operating band for the study. Our simulation results show the mapping of Signal to Interference and Noise Ratio (SINR) to the Channel Quality Indicator (CQI) and thus the best possible selection of modulation and coding scheme in case of perfect channel estimation based system which is found to improve the system throughput.
Author Sahoo, Shubham Somnath
Krishnaswamy, Dilip
Pati, Preeti Samhita
Datta, Raja
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  givenname: Raja
  surname: Datta
  fullname: Datta, Raja
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  organization: IIT Kharagpur,Department of E&ECE,WestBengal,India
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Snippet This study addresses a Machine Learning (ML) based Link Adaptation (LA) scheme for 5G New Radio (NR) wireless networks, which aims to improve the system...
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SubjectTerms Adaptation models
Deep Neural Network (DNN)
Interference
Link Adaptation (LA)
Mathematical model
Modulation
Modulation Coding Scheme (MCS)
Signal to noise ratio
Training data
Wireless networks
Title A Novel Machine Learning Approach for Link Adaptation in 5G Wireless Networks
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