LoRa Performance Analysis with Superposed Signal Decoding

TL;DR

This paper analyzes the use of successive interference cancellation (SIC) in LoRa networks, deriving models for decoding success and demonstrating significant improvements in reliability and user capacity through simulations.

Contribution

It introduces a stochastic geometry-based model for SIC in LoRa networks, providing closed-form expressions and validating them with simulations.

Findings

SIC improves worst-case reliability by up to 34%.

SIC increases the number of served users by 159% in some scenarios.

The model accurately predicts decoding success considering path loss, fading, noise, and interference.

Abstract

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.