Adaptive Multi-Channels Allocation in LoRa Networks
In this paper, we consider an IoT dedicated network corresponding to a non licensed LoRa Low Power Wide Area Network. The LoRa network operates in the unlicensed 868 MHz band within a total bandwidth of 1 MHz divided into 8 orthogonal channels of 125 kHz each. Despite the high level of interference,...
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doaj-33eec8d14e1141fa8aaf03c2ec2924dc2021-03-30T03:51:55ZengIEEEIEEE Access2169-35362020-01-01821417721418910.1109/ACCESS.2020.30407659272315Adaptive Multi-Channels Allocation in LoRa NetworksYi Yu0https://orcid.org/0000-0002-7206-3487Lina Mroueh1https://orcid.org/0000-0002-3051-0760Diane Duchemin2https://orcid.org/0000-0003-2108-9000Claire Goursaud3https://orcid.org/0000-0003-0971-9305Guillaume Vivier4https://orcid.org/0000-0001-5328-7891Jean-Marie Gorce5https://orcid.org/0000-0002-5389-0102Michel Terre6https://orcid.org/0000-0002-6480-3836Institut Supérieur d’Electronique de Paris, Paris, FranceInstitut Supérieur d’Electronique de Paris, Paris, FranceInstitut National des Sciences Appliquées de Lyon (INSA Lyon), Villeurbanne, FranceInstitut National des Sciences Appliquées de Lyon (INSA Lyon), Villeurbanne, FranceSequans Communications, Colombes, FranceInstitut National des Sciences Appliquées de Lyon (INSA Lyon), Villeurbanne, FranceConservatoire National des Arts et Métiers, Paris, FranceIn this paper, we consider an IoT dedicated network corresponding to a non licensed LoRa Low Power Wide Area Network. The LoRa network operates in the unlicensed 868 MHz band within a total bandwidth of 1 MHz divided into 8 orthogonal channels of 125 kHz each. Despite the high level of interference, this network offers long range communications in the order of 2 to 5 km in urban areas and 10 to 30 km in rural areas. To efficiently mitigate this high level of interference, LoRa network essentially relies on a Chirp Spread Spectrum (CSS) modulation and on repetition diversity mechanisms. The LoRa CSS modulation spreads the signal within a band of 125 kHz using 6 possible spreading factors (from 7 to 12) to target data rates (starting from 5 kbps for the closest node to 300 bps for the furthest ones). The repetition diversity mechanisms enable the data recovery when the transmission is subject to bad channel conditions or/and high interference levels. Although the CSS modulation protects edge-cell's devices from the high level of interference induced by nodes in the proximity of the gateway, it fails to protect nodes at the edge of a given SF region and several trials are required to recover the packet. In this paper, we propose an adaptive multi-channels allocation policy that attributes multiple adjacent channels of 125 kHz for nodes situated at the edge of SF zones. We study the impact of this adaptive sub-band allocation on the gateways' intensities, the rate distribution and the power consumption. Our results are based on a statistical characterization of the interference in the network as well as the outage probability in a typical cell.https://ieeexplore.ieee.org/document/9272315/Low Power Wide Area Network (LPWAN)LoRa networkChirp Spread Spectrum modulationInternet of Thingsstochastic geometryspatial Poisson Point Process |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi Yu Lina Mroueh Diane Duchemin Claire Goursaud Guillaume Vivier Jean-Marie Gorce Michel Terre |
spellingShingle |
Yi Yu Lina Mroueh Diane Duchemin Claire Goursaud Guillaume Vivier Jean-Marie Gorce Michel Terre Adaptive Multi-Channels Allocation in LoRa Networks IEEE Access Low Power Wide Area Network (LPWAN) LoRa network Chirp Spread Spectrum modulation Internet of Things stochastic geometry spatial Poisson Point Process |
author_facet |
Yi Yu Lina Mroueh Diane Duchemin Claire Goursaud Guillaume Vivier Jean-Marie Gorce Michel Terre |
author_sort |
Yi Yu |
title |
Adaptive Multi-Channels Allocation in LoRa Networks |
title_short |
Adaptive Multi-Channels Allocation in LoRa Networks |
title_full |
Adaptive Multi-Channels Allocation in LoRa Networks |
title_fullStr |
Adaptive Multi-Channels Allocation in LoRa Networks |
title_full_unstemmed |
Adaptive Multi-Channels Allocation in LoRa Networks |
title_sort |
adaptive multi-channels allocation in lora networks |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
In this paper, we consider an IoT dedicated network corresponding to a non licensed LoRa Low Power Wide Area Network. The LoRa network operates in the unlicensed 868 MHz band within a total bandwidth of 1 MHz divided into 8 orthogonal channels of 125 kHz each. Despite the high level of interference, this network offers long range communications in the order of 2 to 5 km in urban areas and 10 to 30 km in rural areas. To efficiently mitigate this high level of interference, LoRa network essentially relies on a Chirp Spread Spectrum (CSS) modulation and on repetition diversity mechanisms. The LoRa CSS modulation spreads the signal within a band of 125 kHz using 6 possible spreading factors (from 7 to 12) to target data rates (starting from 5 kbps for the closest node to 300 bps for the furthest ones). The repetition diversity mechanisms enable the data recovery when the transmission is subject to bad channel conditions or/and high interference levels. Although the CSS modulation protects edge-cell's devices from the high level of interference induced by nodes in the proximity of the gateway, it fails to protect nodes at the edge of a given SF region and several trials are required to recover the packet. In this paper, we propose an adaptive multi-channels allocation policy that attributes multiple adjacent channels of 125 kHz for nodes situated at the edge of SF zones. We study the impact of this adaptive sub-band allocation on the gateways' intensities, the rate distribution and the power consumption. Our results are based on a statistical characterization of the interference in the network as well as the outage probability in a typical cell. |
topic |
Low Power Wide Area Network (LPWAN) LoRa network Chirp Spread Spectrum modulation Internet of Things stochastic geometry spatial Poisson Point Process |
url |
https://ieeexplore.ieee.org/document/9272315/ |
work_keys_str_mv |
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