Design of energy modulation massive SIMO transceivers via machine learning

摘要

This paper considers a massive single-input multiple-output (SIMO) system, where multiple single-antenna transmitters simultaneously communicate with a receiver equipped with a large number of antennas. Different from the conventional noncoherent transceivers which require a certain level of the statistical information on the channel fading, we propose a joint transceiver design method based on machine learning, requiring a limited number of channel realizations. In the proposed method, the multiple transmitters, the channel, and the receiver are represented with a deep neural network (NN), and an autoencoder is adopted to minimize the end-to-end transmission error probability. Besides, the relationship between the number of training samples and the transmission error probability is analyzed based on the confidence interval method. Simulation results show that the proposed NN-based transceiver achieves lower transmission error probability in typical scenarios, and is more robust against the channel parameters variation compared with the existing methods.