A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation
  Design
Vukan Ninkovic Dejan Vukobratovic Adriano Pastore Carles Anton-Haro
End-to-end design of communication systems using deep autoencoders (AEs) is
gaining attention due to its flexibility and excellent performance. Besides
single-user transmission, AE-based design is recently explored in multi-user
setup, e.g., for designing constellations for non-orthogonal multiple access
(NOMA). In this paper, we further advance the design of AE-based downlink NOMA
by introducing weighted loss function in the AE training. By changing the
weight coefficients, one can flexibly tune the constellation design to balance
error probability of different users, without relying on explicit information
about their channel quality. Combined with the SICNet decoder, we demonstrate a
significant improvement in achievable levels and flexible control of error
probability of different users using the proposed weighted AE-based framework.