Some low power wide area network (LPWAN) developers such as Sigfox, Weightless, and Nwave, have recently commenced the integration of cognitive radio (CR) techniques in their respective LPWAN technologies, generally termed CR‐LPWAN systems. Their objective is to overcome specific limitations associated with LPWANs such as spectra congestion and interference, which in turn will improve the performance of many Internet of Things (IoT)‐based applications. However, in order to be effective under dynamic sensing conditions, CR‐LPWAN systems are typically required to adopt adaptive threshold techniques (ATTs) in order to improve their sensing performance. Consequently, in this article, we have investigated some of these notable ATTs to determine their suitability for CR‐LPWAN systems. To accomplish this goal, first, we describe a network architecture and physical layer model suitable for the effective integration of CR in LPWAN. Then, some specific ATTs were investigated following this model based on an experimental setup constructed using the B200 Universal Software Radio Peripheral kit. Several tests were conducted, and our findings suggest that no single ATT was able to perform best under all sensing conditions. Thus, CR‐LPWAN developers may be required to select a suitable ATT only based on the specific condition(s) for which the IoT application is designed. Nevertheless, some ATTs such as the forward consecutive mean excision algorithm, the histogram partitioning algorithm and the nonparametric amplitude quantization method achieved noteworthy performances under a broad range of tested conditions. Our findings will be beneficial to developers who may be interested in deploying effective ATTs for CR‐LPWAN systems.

中文翻译：

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基于认知无线电的低功耗广域网的自适应阈值技术

一些低功耗广域网（LPWAN）开发人员，例如Sigfox，Weightless和Nwave，最近开始将认知无线电（CR）技术集成到各自的LPWAN技术中，这些技术通常称为CR-LPWAN系统。他们的目标是克服与LPWAN相关的特定限制，例如频谱拥塞和干扰，这反过来又将提高许多基于物联网（IoT）的应用程序的性能。但是，为了在动态感测条件下有效，CR-LPWAN系统通常需要采用自适应阈值技术（ATT），以提高其感测性能。因此，在本文中，我们研究了其中一些著名的ATT，以确定它们是否适合CR-LPWAN系统。为了实现这个目标，首先，我们描述了适合在LPWAN中有效集成CR的网络体系结构和物理层模型。然后，根据使用B200 Universal Software Radio Peripheral套件构建的实验设置，根据该模型研究了一些特定的ATT。进行了几次测试，我们的发现表明，没有一个ATT在所有感测条件下都能发挥最佳性能。因此，可能仅要求CR-LPWAN开发人员根据针对IoT应用程序设计的特定条件选择合适的ATT。尽管如此，一些ATT，例如前向连续均值切除算法，直方图分区算法和非参数幅度量化方法，在广泛的测试条件下仍取得了显着的性能。