Machine Learning Model for Adaptive Modulation of Multi-Stream in MIMO-OFDM System

In this paper, we propose the adaptive modulation (AM) model based on machine learning for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system. Since 5G new radio (NR) system can be used in a large variety of Internet of Things fields than the conventional systems, the AM scheme that adjusts data rate and reliability according to channel condition can be effectively utilized. The conventional AM schemes are implemented by defining modulation schemes to be used according to each channel condition as table in advance. However, since the rule-based AM cannot analyze the communication performance according to the correlation of channels between antennas and the number of transmission modes is exponentially increased according to the number of available modulation schemes and antennas, it is not suitable for 5G NR system. The learning of the proposed AM model is based on the generated training signal by the extracted features from the received signal and assigned label through the performance analysis for signal detection. We focus on the application of deep neural network for AM and cover the precedence method of principal component analysis to improve the performance of the model. The simulations on the classification of optimal transmission mode for the MIMO-OFDM signal demonstrate that the proposed model supports the adaptability according to the condition of complex MIMO channel.