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物联网系统中使用大规模 MIMO 确保频谱效率的有效模式选择的确定。

Determination of Effective Mode Selection for Ensuring Spectrum Efficiency with Massive MIMO in IoT Systems.

机构信息

Department of Electrical and Electronics, Engineering Faculty of Engineering, Duzce University, Duzce 81620, Turkey.

出版信息

Sensors (Basel). 2019 Feb 9;19(3):706. doi: 10.3390/s19030706.

DOI:10.3390/s19030706
PMID:30744087
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6386898/
Abstract

Wireless Sensor Networks (WSNs) based on Internet of Things (IoT) applications are increasing day by day. These applications include healthcare, infrastructure monitoring, smart homes, wearable devices and smart cars. However, considering the fact that many different application areas will emerge in next generation wireless communication systems, efficient use of frequency spectrum is important. Because the whole frequency spectrum is now very crowded, it is important to ensure maximum spectrum efficiency for effective WSNs based on IoT. This study sought to determine which mode more effectively achieves spectrum efficiency for the performance of effective IoT systems under given conditions with respect to the length of the pilot sequence, Time Division Duplexing (TDD) or Frequency Division Duplexing (FDD). The results were obtained by Monte Carlo simulations. To the best of our knowledge, a study of effective mode selection analysis for spectrum efficiency in IoT based systems has not been available in the literature yet. The results of this study are useful for determining the appropriate design conditions for WSNs based on IoT.

摘要

基于物联网 (IoT) 应用的无线传感器网络 (WSN) 日益增多。这些应用包括医疗保健、基础设施监控、智能家居、可穿戴设备和智能汽车。然而,考虑到下一代无线通信系统中将会出现许多不同的应用领域,高效利用频谱非常重要。由于整个频谱现在非常拥挤,因此对于基于物联网的有效 WSN,确保最大频谱效率非常重要。本研究旨在确定在给定条件下,针对导频序列长度,时分双工 (TDD) 或频分双工 (FDD) 哪种模式更能有效地实现频谱效率,从而提高有效的物联网系统性能。通过蒙特卡罗模拟获得了结果。据我们所知,目前文献中还没有关于物联网系统中有效模式选择分析以实现频谱效率的研究。本研究的结果有助于确定基于物联网的 WSN 的适当设计条件。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/eafcbdf45ee4/sensors-19-00706-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/a75eeea794a6/sensors-19-00706-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/7eda6949a4f5/sensors-19-00706-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/48162f6f7dd1/sensors-19-00706-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/9d071ad5fdba/sensors-19-00706-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/eafcbdf45ee4/sensors-19-00706-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/a75eeea794a6/sensors-19-00706-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/7eda6949a4f5/sensors-19-00706-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/48162f6f7dd1/sensors-19-00706-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/9d071ad5fdba/sensors-19-00706-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d8/6386898/eafcbdf45ee4/sensors-19-00706-g005.jpg

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