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低功耗广域网络中长距离的成功概率表征

Success Probability Characterization of Long-Range in Low-Power Wide Area Networks.

作者信息

Kim Yi-Kang, Kim Seung-Yeon

机构信息

Department of Computer Convergence Software, Korea University, Sejong 30019, Korea.

出版信息

Sensors (Basel). 2020 Nov 30;20(23):6861. doi: 10.3390/s20236861.

DOI:10.3390/s20236861
PMID:33266192
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7730519/
Abstract

In low-power wide area networks (LPWAN), a considerable number of end devices (EDs) communicate with the gateway in a certain area, whereas for transmitted data, a low data rate and high latency are allowed. Long-range (LoRa), as one of the LPWAN technologies, considers pure ALOHA and chirp spread spectrum (CSS) in the media access control (MAC) and physical (PHY) layers such that it can improve the energy efficiency while mitigating inter-cell interference (ICI). This paper investigates the system throughput of LoRa networks under the assumption that the interferences between EDs for exclusive regions are ignored using CSS. In order to establish an analytical model for the performance of LoRa, we introduce the pure ALOHA capture model, which is the power threshold model. For this model, we assume that the interfering power is proportional to the length of the time overlapped. In addition, we discuss LoRa gain by comparing the total throughput of LoRa with that of non-CSS.

摘要

在低功耗广域网(LPWAN)中,相当数量的终端设备(ED)在特定区域内与网关进行通信,而对于传输的数据,允许低数据速率和高延迟。作为LPWAN技术之一的远距离(LoRa),在媒体访问控制(MAC)层和物理(PHY)层采用纯ALOHA和啁啾扩频(CSS),以便在减轻小区间干扰(ICI)的同时提高能源效率。本文在忽略使用CSS的专属区域内ED之间干扰的假设下,研究LoRa网络的系统吞吐量。为了建立LoRa性能的分析模型,我们引入了纯ALOHA捕获模型,即功率阈值模型。对于该模型,我们假设干扰功率与重叠时间的长度成正比。此外,我们通过比较LoRa与非CSS的总吞吐量来讨论LoRa增益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/1d1bfcec40a8/sensors-20-06861-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/505e141cfbb9/sensors-20-06861-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/30b959b6506b/sensors-20-06861-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/1be5c691ad0a/sensors-20-06861-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/9d90fda08c6b/sensors-20-06861-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/c29647b180c1/sensors-20-06861-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/00ed220bbc25/sensors-20-06861-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/1d1bfcec40a8/sensors-20-06861-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/505e141cfbb9/sensors-20-06861-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/30b959b6506b/sensors-20-06861-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/1be5c691ad0a/sensors-20-06861-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/9d90fda08c6b/sensors-20-06861-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/c29647b180c1/sensors-20-06861-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/00ed220bbc25/sensors-20-06861-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b691/7730519/1d1bfcec40a8/sensors-20-06861-g007.jpg

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本文引用的文献

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2
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Sensors (Basel). 2016 Sep 9;16(9):1466. doi: 10.3390/s16091466.