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CURE: Enabling RF Energy Harvesting using Cell-Free Massive MIMO UAVs Assisted by RIS
arXiv - CS - Networking and Internet Architecture Pub Date : 2021-07-22 , DOI: arxiv-2107.10412
Alvi Ataur Khalil, Mohamed Y. Selim, Mohammad Ashiqur Rahman

The ever-evolving internet of things (IoT) has led to the growth of numerous wireless sensors, communicating through the internet infrastructure. When designing a network using these sensors, one critical aspect is the longevity and self-sustainability of these devices. For extending the lifetime of these sensors, radio frequency energy harvesting (RFEH) technology has proved to be promising. In this paper, we propose CURE, a novel framework for RFEH that effectively combines the benefits of cell-free massive MIMO (CFmMIMO), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs) to provide seamless energy harvesting to IoT devices. We consider UAV as an access point (AP) in the CFmMIMO framework. To enhance the signal strength of the RFEH and information transfer, we leverage RISs owing to their passive reflection capability. Based on an extensive simulation, we validate our framework's performance by comparing the max-min fairness (MMF) algorithm for the amount of harvested energy.

中文翻译:

CURE:使用由 RIS 辅助的无细胞大规模 MIMO 无人机实现射频能量收集

不断发展的物联网 (IoT) 导致了大量无线传感器的增长,这些传感器通过互联网基础设施进行通信。在使用这些传感器设计网络时,一个关键方面是这些设备的使用寿命和自我可持续性。为了延长这些传感器的使用寿命,射频能量收集 (RFEH) 技术已被证明是有前途的。在本文中,我们提出了一种新的 RFEH 框架 CURE,它有效地结合了无细胞大规模 MIMO (CFmMIMO)、无人机 (UAV) 和可重构智能表面 (RIS) 的优点,为物联网设备提供无缝能量收集. 我们将无人机视为 CFmMIMO 框架中的接入点 (AP)。为了增强 RFEH 的信号强度和信息传输,我们利用 RIS 的被动反射能力。基于广泛的模拟,我们通过比较最大-最小公平 (MMF) 算法获取能量的数量来验证我们框架的性能。
更新日期:2021-07-23
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