GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access
This work addresses performance bottlenecks in NOMA for future wireless networks, presenting an incremental improvement with domain-specific impact.
The paper tackles the problem of real-time detection in non-orthogonal multiple access (NOMA) systems for 5G/6G networks by proposing a neural network architecture that combines linear and non-linear processing, demonstrating superiority over conventional methods through GPU-accelerated implementation and real measurements.
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.