  Non-orthogonal multiple access (NOMA) is an interesting technology that
enables massive connectivity as required in future 5G and 6G networks. While
purely linear processing already achieves good performance in NOMA systems, in
certain scenarios, non-linear processing is mandatory to ensure acceptable
performance. In this paper, we propose a neural network architecture that
combines the advantages of both linear and non-linear processing. Its real-time
detection performance is demonstrated by a highly efficient implementation on a
graphics processing unit (GPU). Using real measurements in a laboratory
environment, we show the superiority of our approach over conventional methods.
