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.

中文翻译：

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LORA 性能分析与叠加信号解码

这封信考虑了使用连续干扰消除 (SIC) 来解码远程 (LoRa) 网络中的叠加信号。我们为 LoRa 网络构建了一个已知的随机几何模型，并包括通过 SIC 恢复碰撞数据包的效果。我们将路径损耗、衰落、噪声和干扰考虑在内，推导出使用 SIC 成功解码数据包的封闭形式表达式，同时我们通过蒙特卡罗模拟验证模型。结果表明，支持 SIC 的 LoRa 网络将最坏情况下的可靠性提高了 34%。我们表明，对于至少一个测试场景，SIC 使在最坏情况可靠性水平相同的情况下服务的用户数量增加了 159%。