Massive device-to-device communication nodes and Internet of Things (IoT) devices are expected to be crucial components in next-generation wireless networks. However, the energy constraint of these nodes presents a challenge since the energy of the batteries is limited. Motivated by this, radio frequency energy harvesting (EH) has been developed as an efficient strategy to overcome the energy constraint of IoT devices and sensor networks. In this paper, a wireless-powered dual-hop amplify-and-forward relaying system, in the absence of a direct link between the source (S) and the destination (D), is considered. It is assumed that a dedicated power beacon (PB) transmits an energy-bearing signal from which the power-constrained S and relay (R) harvest energy. Theoretical derivations of bit error probability, outage probability, and throughput expressions are performed for both linear and non-linear energy harvesting models. Moreover, the theoretical results provided for different system parameters are validated via Monte Carlo simulations. The obtained results reveal the difference between the realistic non-linear EH model and the conventional linear EH model, which overestimates the system performance at high levels of harvested energy. Thus, it leads to misunderstanding the real performance of the EH systems. However, at low levels of harvested energy, both models behave similarly and provide realistic results.

中文翻译：

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非线性/线性能量采集的双跳AF中继性能分析

大规模的设备到设备通信节点和物联网 (IoT) 设备有望成为下一代无线网络的关键组件。然而，由于电池的能量有限，这些节点的能量约束提出了挑战。受此启发，射频能量收集 (EH) 已被开发为克服物联网设备和传感器网络能量限制的有效策略。在本文中，考虑了在源 (S) 和目标 (D) 之间没有直接链路的情况下，无线供电的双跳放大转发中继系统。假设专用功率信标 (PB) 传输能量承载信号，功率受限的 S 和中继 (R) 从中获取能量。误码概率、中断概率的理论推导，对线性和非线性能量收集模型执行吞吐量表达式。此外，通过蒙特卡罗模拟验证了为不同系统参数提供的理论结果。所得结果揭示了现实非线性 EH 模型与传统线性 EH 模型之间的差异，后者高估了高水平收集能量下的系统性能。因此，它会导致误解 EH 系统的真实性能。然而，在收获能量水平较低时，两种模型的行为相似并提供了现实的结果。所得结果揭示了现实非线性 EH 模型与传统线性 EH 模型之间的差异，后者高估了高水平收集能量下的系统性能。因此，它会导致误解 EH 系统的真实性能。然而，在收获能量水平较低时，两种模型的行为相似并提供了现实的结果。所得结果揭示了现实非线性 EH 模型与传统线性 EH 模型之间的差异，后者高估了高水平收集能量下的系统性能。因此，它会导致误解 EH 系统的真实性能。然而，在收获能量水平较低时，两种模型的行为相似并提供了现实的结果。