Multi-class random access wireless network : general results and performance analysis of LoRaWAN |
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Author: | Santos F., F. Helder C.1; Dester, Plínio S.2; Nardelli, Pedro H. J.3,4; |
Organizations: |
1Federal University of Ceará, Quixadá Campus, Quixadá, 63902-580, Brazil 2School of Electrical and Computer Engineering, University of Campinas, Brazil 3LUT University, Finland
4University of Oulu, Finland
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Format: | article |
Version: | published version |
Access: | open |
Online Access: | PDF Full Text (PDF, 1.1 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2022111866125 |
Language: | English |
Published: |
Elsevier,
2022
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Publish Date: | 2022-11-18 |
Description: |
AbstractThis paper presents new analytical results for evaluating the ALOHA-like multi-class random access wireless network’s performance. The proposed model is motivated by the growth of low-power wireless networks that employ random access protocols. In particular, we compare our analytical formulation with system-level simulations of Long Range (LoRa) technology. We show that the proposed formulation provides an accurate approximation of LoRaWAN performance capturing its main trade-offs. The main contributions are (i) an extensive analysis of the impact of different LoRa spreading factors (SFs) allocation strategies, including area intersection among SFs, which is little explored in the literature and represents the optimal approach under some conditions; and (ii) the optimal proportion of users that maximizes the network throughput for each class and for each allocation strategy considered in the paper. see all
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Series: |
Ad hoc networks |
ISSN: | 1570-8705 |
ISSN-E: | 1570-8713 |
ISSN-L: | 1570-8705 |
Volume: | 135 |
Article number: | 102946 |
DOI: | 10.1016/j.adhoc.2022.102946 |
OADOI: | https://oadoi.org/10.1016/j.adhoc.2022.102946 |
Type of Publication: |
A1 Journal article – refereed |
Field of Science: |
213 Electronic, automation and communications engineering, electronics |
Subjects: | |
Funding: |
This paper is partly supported by Foundation for Research Support of the State of São Paulo, Brazil (Grant No. 2017/21347-0). This work as also financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. This paper is partly supported by Academy of Finland via: (a) ee-IoT n.319009, (b) FIREMAN consortium CHIST-ERA-17-BDSI-003/n.326270, and (c) EnergyNet Fellowship n.321265/n.328869. This paper is partly supported by 6G Flagship (Grant n.318927), and ee-IoT (n.319008) and FIREMAN consortium ( CHIST-ERA-17-BDSI-003/n.24303093). |
Academy of Finland Grant Number: |
318927 319008 |
Detailed Information: |
318927 (Academy of Finland Funding decision) 319008 (Academy of Finland Funding decision) |
Copyright information: |
© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |