Indoor long range wide area network (LoRaWAN) propagation is shaped by structural and time-varying environmental factors, which limit single-slope log-distance models and the standard log-normal shadowing assumption. We propose an environment-conditioned path loss framework that augments a log-distance multi-wall baseline with co-recorded environmental covariates (relative humidity, temperature, carbon dioxide, particulate matter, and barometric pressure) and receiver-reported signal-to-noise, and we validate both the mean and the residual law statistically. The approach is evaluated on a 12-month campaign in an eighth-floor office (240 m^2) using time-blocked 5-fold cross-validation and a chronological hold-out. Across parametric regressors (regularized multiple linear regression (MLR), conjugate Bayesian linear regression, and a selective quadratic MLR extension on continuous predictors), the selective polynomial mean improves out-of-sample accuracy, reducing cross-validated root mean square error from 8.23 to 7.38 dB and increasing R^2 from 0.81 to 0.84. Out-of-fold (OOF) residuals are distinctly non-Gaussian and are best summarized by a compact 3-component Gaussian mixture with a sharp core and a light, broad tail. Finally, we translate prediction error into reliability by prescribing the fade margin as the upper-tail percentile of OOF errors, attaching moving-block bootstrap uncertainty, and validating the resulting outage on a held-out set. At a 1% outage target (99% reliability), the polynomial model requires 25.73 dB versus 27.79 to 28.05 dB for linear baselines, enabling tighter indoor massive Internet of Things link budgets aligned with sixth-generation reliability targets under energy constraints.

翻译：室内远距离广域网（LoRaWAN）传播受结构性及时变环境因素的影响，这使得单斜率对数距离模型与标准对数正态阴影假设存在局限性。我们提出一种环境条件化路径损耗框架，该框架在对数距离多墙基线模型基础上，融入共记录的环境协变量（相对湿度、温度、二氧化碳、颗粒物浓度及大气压）与接收机报告的信噪比，并从统计上验证了均值与残差分布。该方法基于12个月的连续观测（8楼240平方米办公区域），采用时间分块5折交叉验证及按时间顺序的留出测试集进行评估。在参数回归器（正则化多元线性回归、共轭贝叶斯线性回归及连续预测变量的选择性二次多元线性回归扩展）中，选择性多项式均值提升了样本外精度，将交叉验证均方根误差从8.23 dB降至7.38 dB，决定系数R²从0.81提升至0.84。折外残差呈现显著非高斯特性，最佳拟合为包含尖锐核心与轻度宽尾的三分量紧凑高斯混合模型。最终，我们将预测误差转化为可靠性指标：将折外误差的上尾百分位数定义为衰落余量，附加运动分块bootstrap不确定性估计，并在留出测试集上验证其导致的通信中断概率。在1%中断目标（99%可靠性）下，多项式模型仅需25.73 dB衰落余量（线性基线模型需27.79-28.05 dB），从而在能效约束下实现与第六代通信可靠性目标相一致的更紧凑的室内大规模物联网链路预算。