Constellation Design for Multi-user Communications with Deep Learning

Abstract

In the simple form, a communication system includes a transmitter and a receiver. In the transmitter, it transforms the one-hot vector message to produce a transmitted signal. In general, the transmitter demands restrictions on the transmitted signal. The channel is defined by the conditional probability distribution function. On receiving of the transmitted signal with noise, the receiver appears to apply the transformation to generate the estimate of one hot vector message. We can regard this simplest communication system as a specific case of autoencoder from a deep learning perspective. In our case, autoencoder used to learn the representations of the one-hot vector which are robust to the noise channel and can be recovered at the receiver with the smallest probability of error. Our task is to make some improvements on the autoencoder systems. We propose different schemes depending on the different cases. We propose a method based on optimization of softmax and introduce the L1/2 regularization in MSE loss function for SISO case and MIMO case, separately. The simulation shows that both our optimized softmax function method and L1/2 regularization loss function have a better performance than the original neural network framework.