In multicell massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks, base stations (BSs) with multiple antennas deliver their radio frequency energy in the downlink, and Internet-of-Things (IoT) devices use their harvested energy to support uplink data transmission. This paper investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer (WPT). To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information (CSI) are considered, and their corresponding EE performance is analyzed. Under quality-of-service (QoS) requirements, an EE maximization problem is formulated, which is non-trivial due to non-convexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex, and then, develop a distributed alternating direction method of multipliers (ADMM)- based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.

翻译：在多细胞大规模多输出多输出(MIMO)非垂直多输出(NOMA)网络中,拥有多个天线的基准台(BS)在下行链路中提供无线电频率能量,而互联网Tings(IoT)设备则利用所收获的能量支持上行数据传输。本文调查了使用无电传输(WPT)的多细胞大型MIMO NOMA网络的能源效率问题。为了尽量扩大网络的EEE,我们提出了一个新的联合动力、时间、天线选择和子载体资源分配方案,这可以适当分配能源采集和数据传输的时间。考虑了完美和不完善的频道状态信息,并分析了它们相应的 EEE绩效。在服务质量(Qos)要求下,提出了EE最大化问题,由于非电离子传输功能转移(WPT),这是非三角的。我们首先采用了非线性部分编程方法,将问题转换为 convex,然后开发一种分布式的变换方向方法(ADMMM)方法,可以比较以Simermainal 为基础的倍化方法。