论文标题
洛拉性能分析，具有超封码信号解码
LoRa Performance Analysis with Superposed Signal Decoding
论文作者
论文摘要
本文考虑使用连续的干扰取消（SIC）在远距离（LORA）网络中解释超封闭信号。我们通过用于洛拉网络的已知随机几何模型建立，包括通过SIC恢复碰撞数据包的效果。我们通过SIC逐渐丢失，褪色，噪声和干扰来得出封闭形式的表达式，以成功地解码数据包，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网络提高了最差的可靠性高达34％。我们表明，对于至少一种测试方案，SIC增加了159％的服务用户数量，具有相同最差的可靠性水平。
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.
