Spatiotemporal modelling of multi-gateway LoRa networks with imperfect SF orthogonality
File(s)
Accepted version
Author(s)
Bouazizi, Yathreb
Benkhelifa, Fatma
McCann, Julie
Type
Conference Paper
Abstract
Meticulous modelling and performance analysis ofLow-Power Wide-Area (LPWA) networks are essential for largescale dense Internet-of-Things (IoT) deployments. As Long Range(LoRa) is currently one of the most prominent LPWA tech-nologies, we propose in this paper a stochastic-geometry-basedframework to analyse the uplink transmission performance ofa multi-gateway LoRa network modelled by a Matern ClusterProcess (MCP). The proposed model is first to consider alltogether the multi-cell topology, imperfect spreading factor (SF)orthogonality, random start times, and geometric data arrivalrates. Accounting for all of these factors, we initially develop theSF-dependent collision overlap time function for any start timedistribution. Then, we analyse the Laplace transforms of intra-cluster and inter-cluster interference, and formulate the uplinktransmission success probability. Through simulation results, wehighlight the vulnerability of each SF to interference, illustratethe impact of parameters such as the network density, and thepower allocation scheme on the network performance. Uniquely,our results shed light on when it is better to activate adaptivepower mechanisms, as we show that an SF-based power allocationthat approximates LoRa ADR, negatively impacts nodes nearthe cluster head. Moreover, we show that the interfering SFsdegrading the performance the most depend on the decodingthreshold range and the power allocation scheme.
Date Issued
2021-01-25
Date Acceptance
2020-08-17
Citation
2021, pp.1-7
Publisher
IEEE
Start Page
1
End Page
7
Copyright Statement
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Identifier
https://ieeexplore.ieee.org/document/9322640/authors#authors
Source
2020 IEEE Global Communications Conference (also virtual)
Subjects
eess.SP
eess.SP
Publication Status
Published
Start Date
2020-12-07
Finish Date
2020-12-11
Coverage Spatial
Taipei, Taiwan
Date Publish Online
2021-01-25