I. J. of Electrical & Electronics Research
Research Article | 
MCS Selection Based on Convolutional Neural Network in TDD System
Author(s): Jeong-Eun Oh1, A-Min Jo2 and Eui-Rim Jeong3*
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 11, Issue 2
Publisher : FOREX Publication
Published : 30 June 2023
e-ISSN : 2347-470X
Page(s) : 485-489
Abstract
In this paper, a convolutional neural network (CNN) is proposed for selecting modulation and coding schemes (MCSs) at the time of future transmission in time-division-duplex (TDD) systems. The proposed method estimates the signal-to-noise ratio (SNR) obtained by the average of the equalizer’s output in the orthogonal frequency division multiplexing (OFDM) system and records it to select the most suitable MCS for future transmission. Two methods are proposed: one that directly selects an MCS and one that predicts the SNR first before selecting an MCS. The conventional method commonly used is to select an MCS based on the SNR of the most recently received signal. Computer simulations show that the outage probability and throughput of all proposed methods (direct and indirect) are superior to conventional methods (recent value). Shorter SNR sampling periods perform better than longer ones, and the accuracy of MCS selection decreases as mobile speed increases
Keywords: MCS Selection, TDD, CNN, Classification, Regression.
Jeong-Eun Oh, Department of Mobile Convergence Engineering, Hanbat National University, Daejeon, Republic of Korea; Email: wjddms1199@gmail.com
A-Min Jo, Department of Mobile Convergence Engineering, Hanbat National University, Daejeon, Republic of Korea; Email: whdkals18@gmail.com
Eui-Rim Jeong*, Department of Artificial Intelligence Software, Hanbat National University, Daejeon, Republic of Korea; Email: erjeong@hanbat.ac.kr
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