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

摘要

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.