Non-orthogonal multiple access (NOMA) is one of the promising techniques for spectrum efficiency in wireless networks. In this paper, we consider an uplink NOMA cognitive system, where the secondary users (SUs) can jointly transmit data to the cognitive base station (CBS) over the same spectrum resources. Thereafter, successive interference cancellation is applied at the CBS to retrieve signals transmitted by the SUs. In addition, the energy-constrained problem in wireless networks is taken into account. Therefore, we assume that the SUs are powered by a wireless energy harvester to prolong their operations; meanwhile, the CBS is equipped with a traditional electrical supply. Herein, we propose an actor–critic reinforcement learning approach to maximize the long-term throughput of the cognitive network. In particular, by interacting and learning directly from the environment over several time slots, the CBS can optimally assign the amount of transmission energy for each SU according to the remaining energy of the SUs and the availability of the primary channel. As a consequence, the simulation results verify that the proposed scheme outperforms other conventional approaches (such as Myopic NOMA and OMA), so the system reward is always maximized in the current time slot, in terms of overall throughput and energy efficiency.

非正交多址访问（NOMA）是无线网络中提高频谱效率的有前途的技术之一。在本文中，我们考虑了上行NOMA认知系统，其中二级用户（SU）可以在相同频谱资源上联合将数据传输到认知基站（CBS）。此后，在CBS处应用连续的干扰消除来检索由SU发送的信号。另外，考虑了无线网络中的能量受限问题。因此，我们假设SU由无线能量收集器供电以延长其运行时间；同时，CBS配备了传统的电源。本文中，我们提出了一种行为者-批评强化学习方法，以最大化认知网络的长期吞吐量。特别是，通过直接在几个时隙上从环境进行交互和学习，CBS可以根据SU的剩余能量和主信道的可用性，为每个SU最佳分配传输能量。结果，仿真结果验证了所提出的方案优于其他常规方法（例如近视NOMA和OMA），因此在总吞吐量和能源效率方面，系统回报始终在当前时隙中最大化。