GRU Based MCS Selection in Tactical Vehicle Communication

In this paper, we propose optimal modulation coding scheme (MCS) selection based on Gated Recurrent Unit (GRU) for one-to-one communication between tactical vehicles. The communication between tactical vehicles assumes orthogonal frequency division multiplexing (OFDM) and performs bidirectional communication with time division duplexing (TDD) manner. Since the TDD system uses the same frequency for transmitting and receiving, the bidirectional communication channels are the same. Based on the Signal-to-Noise Ratio (SNR) measuring from the received signal, the MCS at the future transmission time is predicted, utilizing a Gated Recurrent Unit (GRU), which is a type of Recurrent Neural Network (RNN). Existing methods for predicting the MCS from the received SNR include the mean value method and the recent value method, and the method based on the convolutional neural network (CNN). Based on the computer simulation results, the proposed GRU-based RNN technique shows a lower outage probability of communication than all conventional methods while provides the highest throughput.