Imperial College London

ProfessorJulieMcCann

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems
 
 
 
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Contact

 

+44 (0)20 7594 8375j.mccann Website

 
 
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Location

 

258ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Qin:2017:10.1109/ICC.2017.7996589,
author = {Qin, Z and Liu, Y and Li, GY and McCann, JA},
doi = {10.1109/ICC.2017.7996589},
publisher = {IEEE},
title = {Modelling and analysis of low-power wide-area networks},
url = {http://dx.doi.org/10.1109/ICC.2017.7996589},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - We investigate the uplink transmission performance of low-power wide-area networks (LPWANs) with regards to coexisting radio modules using LoRa as an example. In doing so we adopt a new topology to model the network where the node locations of the network of focus (LoRa) follow a Poisson cluster process (PCP) while other coexisting interfering radio modules follow a Poisson point process (PPP). To characterize the performance of the proposed model as well as obtain insights, both analytical and closed-form approximated expressions for coverage probability are derived. Based on this, area spectrum efficiency, and energy efficiency are further characterized. These results demonstrate the degree to which the performance, with regard to the aforementioned metrics, is capable of being enhanced through varying the density of the deployment of LoRa nodes around each LoRa receiver. Moreover, simulation results unveil that an optimal value of active LoRa nodes in each cluster exists that maximizes area spectrum efficiency.
AU - Qin,Z
AU - Liu,Y
AU - Li,GY
AU - McCann,JA
DO - 10.1109/ICC.2017.7996589
PB - IEEE
PY - 2017///
SN - 1550-3607
TI - Modelling and analysis of low-power wide-area networks
UR - http://dx.doi.org/10.1109/ICC.2017.7996589
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000424872101108&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/62736
ER -