Constellation design with deep learning for downlink non-orthogonal multiple access

The non-orthogonal multiple access (NOMA) technique is considered as a key component for the next generation cellular system. In downlink NOMA, the constellation of several users are superposed for transmission. The resulting super-constellation needs to be carefully designed for allowing recovering of the data at the receiver side. A deep learning method for constellation optimization is proposed here in the context of downlink NOMA communications. The method is based on an analogy between auto-encoders, a powerful tool in neural networks, and communication systems. Simulation results have verified the effectiveness of this method for both constellation design and optimization of the individual receivers of the users. The optimized encoder/decoder can be successfully combined with iterative error-correction devices such as turbo-codes or LDPC and can be integrated in current communication systems. This technique is quite general and can be used for point-to-point communication as well as for multi-user access under various channel conditions.