Non-orthogonal multiple access (NOMA) technology provides an effective solution to massive access with a high data rate demand in new-generation mobile networks. The paper combinations with NOMA and simultaneous wireless information and power transfer (SWIPT) relay to maximize the sum rate in the downlink system. To that end, it is critical how to select effectively users access system and power allocation for the access user. This paper proposes a user selection and dynamic power allocation (USDPA) scheme in the NOMA-SWIPT relay system based on neural network because traditional optimization methods have difficulty solving nonlinear and non-convex problems. We establish a user selection network utilizing a deep neural network (DNN) and propose a power allocation network using deep reinforcement learning. The simulation results show that the proposed scheme achieves better performance than other related schemes, especially for high quality of service requirements.

非正交多路访问（NOMA）技术为新一代移动网络中对高数据速率需求的大规模访问提供了有效的解决方案。论文结合了NOMA和同时进行的无线信息和功率传输（SWIPT）中继，以最大化下行链路系统中的总速率。为此，至关重要的是如何有效地选择用户接入系统和接入用户的电源分配。由于传统的优化方法难以解决非线性和非凸性问题，本文提出了一种基于神经网络的NOMA-SWIPT中继系统用户选择和动态功率分配（USDPA）方案。我们利用深度神经网络（DNN）建立用户选择网络，并提出利用深度强化学习的功率分配网络。