Multiuser detection in hybrid Non-Orthogonal Multiple Access (NOMA) using machine learning.

This data presents an ANN model explicitly for MUD in NOMA networks. Our method capitalizes on the power of deep neural networks to discern multi-user signals and filters them out which alleviated disadvantages in classical detection methods. Through training, the ANN recognizes the patterns and characteristics of received signals. This new method improves detection accuracy and spectrum utilization. The integration of ANNs with NOMA systems is a good innovation in wireless communication technology providing better performance for massive user scenarios.