Transmit Antenna Selection Based on SNR prediction in TDD Systems Using Convolutional Neural Network

Abstract

This paper proposes a method for predicting future signal-to-noise ratio (SNR) in a time-division-duplexing (TDD) mobile communication environments using a convolutional neural network (CNN). The communication system uses multiple receive antennas and transmit using only one or two antennas among them. A CNN model is proposed to predict the SNR at a future transmission time based on past SNRs received from multiple antennas. The probability of reception at a certain is set to 10-100%. In case that SNR cannot be measured due to the absence of reception, linear interpolation is performed using two adjacent recorded SNRs. If even two adjacent SNRs do not exist, the SNR is set to 0dB. Comparing the predicted SNRs at multiple antennas, the antenna with the highest SNR value is selected for future transmission. To verify antenna selection accuracy, computer simulation is conducted. The simulation results substantiate the superiority of the proposed method over conventional method in single antenna selection. Regarding multi-antenna selection, the proposed method demonstrates diminished accuracy relative to conventional methods at lower speeds. Nevertheless, a comprehensive evaluation considering the root mean square error (RMSE) demonstrates the overall superiority of the proposed method across all speeds.