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首页> 外文期刊>Internet of Things Journal, IEEE >Performance Analysis for Multihop Cognitive Radio Networks With Energy Harvesting by Using Stochastic Geometry
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Performance Analysis for Multihop Cognitive Radio Networks With Energy Harvesting by Using Stochastic Geometry

机译：利用随机几何与能量收集多跳认知无线电网络的性能分析

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摘要

Cognitive multihop relaying has been widely considered for device-to-device (D2D) communications for applications in the physical layer of the Internet of Things. In this article, we construct a multihop cellular D2D communications system model with energy harvesting (EH) in underlay cognitive radio networks. The locations of primary user equipments (PUEs) and cellular base stations are considered as a Poisson point process in this model. The transmit power of secondary devices is collected from the power beacon with time-switching EH policy. Two charging policies for different applications are considered in this article. Then, the end-to-end outage probability analysis expressions of these two scenarios for the transmission scheme subject to interferences from PUEs are derived. The optimal harvesting time ratio is obtained to get the maximum capacity for end-to-end D2D communications. The analytical results are validated by performing the Monte Carlo simulation of the end-to-end outage probability, which is based on the half-duplex transmission scheme. The results of this article provide a potential pathway to reduce reliance on grid or battery energy supplies and, hence, further strengthen the benefits for the environment and deployment of future smart devices.
机译：认知多跳中继已被广泛考虑用于设备的设备（D2D）通信，用于物理层的物理层的应用。在本文中，我们在底层认知无线电网络中构建了具有能量收集（EH）的多跳蜂窝D2D通信系统模型。主要用户设备（PU）和蜂窝基站的位置被认为是该模型中的泊松点过程。通过时间切换EH策略从电源标识收集辅助设备的发射功率。本文考虑了两个用于不同应用程序的充电策略。然后，推导出通过PUE干扰的传输方案的这两种场景的端到端中断概率分析表达式。获得最佳收获时间比以获得最大端到端D2D通信的最大容量。通过执行基于半双工传输方案的端到端中断概率的蒙特卡罗模拟来验证分析结果。本文的结果提供了潜在的途径，以减少对电网或电池能量供应的依赖，因此进一步加强了对环境和部署未来智能设备的益处。

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