1. [Publications](/index.php/publications) 2. GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access # GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access ![](/sites/default/files/publications/9909865-fig-1-source-large.gif) 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 accept-able 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. ## Authors Daniel Schäufele (Fraunhofer Heinrich Hertz Institute) [Guillermo Marcus](/index.php/person/guillermo-marcus) [Nikolaus Binder](/index.php/person/nikolaus-binder) Matthias Mehlhose (Fraunhofer Heinrich Hertz Institute) [Alex Keller](/index.php/person/alex-keller) Slawomir Stańczak (Fraunhofer Heinrich Hertz Institute) ## Publication Date Monday, August 29, 2022 ## Published in [2022 30th European Signal Processing Conference (EUSIPCO)](https://ieeexplore.ieee.org/document/9909865) ## Research Area [Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence) [Telecommunications](/index.php/research-area/telecommunications) ## External Links [ArXiv Paper](http://arxiv.org/abs/2206.05998) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .