A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design
This work addresses the problem of multi-user communication system design for researchers and engineers, offering an incremental advancement over existing AE-based methods.
The paper tackled the design of downlink non-orthogonal multiple access (NOMA) constellations using deep autoencoders by introducing a weighted loss function to balance error probabilities across users without explicit channel knowledge, demonstrating significant improvements in achievable levels and flexible control.
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.