Autoencoder based Communication System using Multi-Dimensional Constellations

This paper proposes an autoencoder based multiple-input multiple-output (MIMO) communication system. The proposed autoencoder learns and optimizes for only line of sight (LOS) component of Rician channel. In addition, we adopt multi-dimensional constellation (MDC) in autoencoder, where it is obtained during learning process of autoencoder by adjusting hyperparameter. Simulation results show that our proposed autoencoder using MDC achieves better symbol error rate (SER) performance compared to conventional communication system which uses quadrature amplitude modulation (QAM) constellation. Furthermore, we confirmed that although proposed autoencoder is learned for only LOS component, it can be applied to random Rician flat fading channels with fading components and channel variation terms.