论文标题

在通道瑕疵下的能源收集物联网网络的保密限制

Secrecy Limits of Energy Harvesting IoT Networks under Channel Imperfections

论文作者

Jameel, Furqan, Chang, Zheng, Jantti, Riku

论文摘要

同时无线信息和电力传输(SWIPT)最近从学术界和行业中收集了很多研究兴趣,这是能源收集互联网(IoT)网络的关键推动者。由于此类网络的许多不断增长的用例,重要的是要从物理层安全性(PLS)的角度研究其性能限制。有了这一意图,这项工作旨在为Rician和Nakagami-M褪色的沟通链接提供对Swipt系统的千古保密能力的新颖分析。为了对系统进行现实评估,已经考虑了基于SWIPT的物联网系统的不同接收器设计的通道估计的缺陷。随后,提供了对经过的方案的Ergodic保密能力的封闭形式表达式,然后通过广泛的模拟验证。结果表明,由于不完善的信噪比值(SNR)值不完善的通道估计,出现误差上限。更重要的是,尽管主链路SNR增加了,但不同渠道条件下的保密能力仍停止超过一定限制。还进行了对秘密能源权衡的深入分析,并为不完美和完美的渠道估计案例提供了比较。作为物联网网络连续演变的一部分,本工作中提供的结果可以帮助识别在存在多个窃听者的情况下,在有多个窃听者的情况下识别物联网网络的保密限制。

Simultaneous wireless information and power transfer (SWIPT) has recently gathered much research interest from both academia and industry as a key enabler of energy harvesting Internet-of-things (IoT) networks. Due to a number of growing use cases of such networks, it is important to study their performance limits from the perspective of physical layer security (PLS). With this intent, this work aims to provide a novel analysis of the ergodic secrecy capacity of a SWIPT system is provided for Rician and Nakagami-m faded communication links. For a realistic evaluation of the system, the imperfections of channel estimations for different receiver designs of the SWIPT-based IoT systems have been taken into account. Subsequently, the closedform expressions of the ergodic secrecy capacities for the considered scenario are provided and, then, validated through extensive simulations. The results indicate that an error ceiling appears due to imperfect channel estimation at high values of signal-to-noise ratio (SNR). More importantly, the secrecy capacity under different channel conditions stops increasing beyond a certain limit, despite an increase of the main link SNR. The in-depth analysis of secrecy-energy trade-off has also been performed and a comparison has been provided for imperfect and perfect channel estimation cases. As part of the continuous evolution of IoT networks, the results provided in this work can help in identifying the secrecy limits of IoT networks in the presence of multiple eavesdroppers.

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