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Cognitive Radio in Low Power Wide Area Network for IoT Applications: Recent Approaches, Benefits and Challenges

机译:用于物联网应用的低功耗广域网中的认知无线电:最近的方法,福利和挑战

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Some recent survey statistics suggest that low power wide area networks (LPWANs) are fast becoming the most prevalent communication platform used in many applications of the Internet of Things (IoT). However, because most LPWANs are generally deployed in the presently congested industrial, scientific, and medical bands, they are invariably plagued by problems associated with spectral congestion, such as increased interference, reduced data rates, and spectra inefficiency. These problems are solvable by integrating cognitive radio (CR) technologies in LPWAN (termed CR-LPWAN), for which some pioneering solutions now exist in the literature. Consequently, this article takes an early look at some of these pioneering efforts pertaining to the development of CR-LPWAN systems. We discuss a general network architecture and a physical layer front-end model suitable for CR-LPWAN systems. Then, some notable state-of-the-art approaches for CR-LPWAN systems are discussed. Potential advantages of CR-LPWAN systems for IoT-based applications are also presented, and this article closes with a few research challenges and future research directions in this regard. This article aims to serve as a starting point for most budding researchers who may be interested in the development of effective and efficient CR-LPWAN systems for the enhancement of different IoT-based applications.
机译:一些最近的调查统计数据表明,低功率广域网(LPWANS)快速成为最普遍的通信平台,用于互联网(物联网)的许多应用中使用。然而,由于大多数LPWAN通常在目前拥挤的工业,科学和医学乐队中部署,因此它们总是被与光谱充血相关的问题困扰,例如增加的干扰,减少数据速率和光谱效率低。这些问题是通过集成LPWAN(称为CR-LPWAN)的认知无线电(CR)技术来解决这些问题,其中一些开创性解决方案现在存在于文献中。因此,本文早日了解了与CR-LPWAN系统的开发有关的一些开创性努力。我们讨论了适用于CR-LPWAN系统的一般网络架构和物理层前端模型。然后,讨论了一些关于CR-LPWAN系统的一些显着的最先进的方法。还提出了CR-LPWAN系统对基于物联网申请的潜在优势,本文在这方面缩短了一些研究挑战和未来的研究方向。本文旨在作为大多数发芽研究人员的起点,这些研究人员可能有兴趣开发有效和高效的CR-LPWAN系统,以提高不同的基于物联网的应用程序。

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