  This paper 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.
