LoRa Performance Analysis with Superposed Signal Decoding

This letter considers the use of successive interference cancellation (SIC) to decode superposed signals in Long Range (LoRa) networks. We build over a known stochastic geometry model for LoRa networks and include the effect of recovering colliding packets through SIC. We derive closed-form expressions for the successful decoding of packets using SIC taking path loss, fading, noise, and interference into account, while we validate the model by means of Monte Carlo simulations. Results show that SIC-enabled LoRa networks improve worst-case reliability by up to 34%. We show that, for at least one test scenario, SIC increases by 159% the number of served users with the same worst-case reliability level.

Authors:
de Souza Sant’Ana Jean Michel, Hoeller Arliones, Souza Richard Demo, Alves Hirley, Montejo-Sánchez Samuel

Publication type:
A1 Journal article – refereed

Place of publication:

Keywords:
Internet of Things, LoRaWAN, stochastic geometry, successive interference cancellation

Published:
2 July 2020

Full citation:
J. M. de Souza Sant’Ana, A. Hoeller, R. D. Souza, H. Alves and S. Montejo-Sánchez, “LoRa Performance Analysis with Superposed Signal Decoding,” in IEEE Wireless Communications Letters, vol. 9, no. 11, pp. 1865-1868, Nov. 2020, doi: 10.1109/LWC.2020.3006588

DOI:
https://doi.org/10.1109/LWC.2020.3006588

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2020120399287