论文标题
非正交多访问中的GPU加速机器学习
GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access
论文作者
论文摘要
非正交多访问（NOMA）是一项有趣的技术，可以根据未来的5G和6G网络的要求实现大规模连通性。尽管纯线性处理已经在NOMA系统中实现了良好的性能，但在某些情况下，非线性处理是必须的，以确保可接受的性能。在本文中，我们提出了一种结合线性和非线性处理的优势的神经网络结构。在图形处理单元（GPU）上的高效实现证明了其实时检测性能。使用实验室环境中的实际测量值，我们显示了方法比常规方法的优越性。
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.
