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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Vydané v:	2020 2nd PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS) s. 1 - 2
Hlavní autori:	Pati, Preeti Samhita, Sahoo, Shubham Somnath, Krishnaswamy, Dilip, Datta, Raja
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 08.11.2020
Predmet:	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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Popis
Shrnutí:	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.
DOI:	10.1109/PhDEDITS51180.2020.9315299