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Backscatter-Assisted Collision-Resilient LoRa Transmission.
Xiao, Fei; Kuang, Wei; Dong, Huixin; Wang, Yiyuan.
Afiliação
  • Xiao F; School of Navigation, Wuhan University of Technology, Wuhan 430074, China.
  • Kuang W; School of Management, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Dong H; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Wang Y; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel) ; 22(12)2022 Jun 13.
Article em En | MEDLINE | ID: mdl-35746253
Low-power wide-area networks (LPWANs), such as LoRaWAN, play an essential role and are expanding quickly in miscellaneous intelligent applications. However, the collision problem is also expanding significantly with the mass promotion of LPWAN nodes and providing collision-resilient techniques that are urgently needed for these applications. This paper proposes BackLoRa, a lightweight method that enables collision-resilient LoRa transmission with extra propagation information provided by backscatter tags. BackLoRa uses several backscatter tags to create multipath propagation features related to the LoRa nodes' positions and offers a lightweight algorithm to extract the feature and correctly distinguish each LoRa node. Further, BackLoRa proposes a quick-phase acquisition algorithm with low time complexity that can carry out the iterative recovery of symbols for robust signal reconstructions in low-SNR conditions. Finally, comprehensive experiments were conducted in this study to evaluate the performance of BackLoRa systems. The experimental results show th compared with the existing scheme, our scheme can reduce the symbol error rate from 65.3% to 5.5% on average and improve throughput by 15× when SNR is -20 dB.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article