MCS Selection Based on Convolutional Neural Network in TDD System

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

Reinforcement Learning Approach for Adaptive C-V2X Resource Management

“…The estimated combined channel matrix and the noise standard deviation are used as input features to train the DCNN. In [7], convolutional neural networks (CNNs) are proposed for MCS selection at the time of transmission in time-division-duplex (TDD) systems. The proposed method determines the most suitable modulation and coding scheme (MCS) for future transmission based on the shifts in received signal-to-noise ratio (SNR) with time.…”