論文

査読有り
2020年12月

Bayesian-based channel quality estimation method for LoRaWAN with unpredictable interference

Proceedings of IEEE Global Communications Conference (GLOBECOM)
  • Daichi Kominami
  • ,
  • Yohei Hasegawa
  • ,
  • Kosuke Nogami
  • ,
  • Hideyuki Shimonishi
  • ,
  • Masayuki Murata

記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/GLOBECOM42002.2020.9322136

© 2020 IEEE. The 'Internet of things' has become a common term, and low-power wide-area (LPWA) technology is attracting much attention as one of its elemental technologies. LPWA achieves wide-area communication without consuming much energy, allowing various data sensing and gathering applications. LoRa is an LPWA communication technology that uses unlicensed bands. Because it is possible to build a self-managed network with LoRa, many LoRa-based services will be scattered in the same area without an overall administrator. As a result, the communication performance of LoRa may degrade due to unintended radio interference. Unfortunately, many LPWA techniques, including LoRa, have low data rates, making it difficult to gather sufficient control information to avoid such degradation of communication performance. In this paper, we propose a method for estimating network congestion states through successive estimation using Bayesian updates of prior distributions. Computer simulations show the network state can be estimated by our proposed method with accumulating a little control information.

リンク情報
DOI
https://doi.org/10.1109/GLOBECOM42002.2020.9322136
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85100425133&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85100425133&origin=inward
ID情報
  • DOI : 10.1109/GLOBECOM42002.2020.9322136
  • SCOPUS ID : 85100425133

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