用置信预测提升无线模型不确定性估计精度
Uncertainty Estimation for Path Loss and Radio Metric Models
- 采用置信预测系统构建95%置信区间，提升模型可靠性
- 在多城市数据上验证，覆盖率高且泛化能力强
- 识别困难样本，降低误差，助力网络规划决策
本研究采用置信预测（CP）中的置信预测系统（CPS），对一系列基于机器学习的无线度量模型[1]及二维地图型路径损耗模型[2]进行不确定性估计。通过多种难度评估器，构建了统计稳健的95%置信预测区间（PIs）。实验表明，基于多伦多数据训练的CPS模型可有效泛化至温哥华和蒙特利尔等其他城市，保持高覆盖率与可靠性。同时，所用难度评估器能识别困难样本，随着数据集难度降低，均方根误差（RMSE）显著下降。结果表明，CPS在无线网络建模中提供了可扩展且可靠的不确定性估计，为网络规划、运维与频谱管理提供重要支持。
原文摘要 · Abstract (English)
This research leverages Conformal Prediction (CP) in the form of Conformal Predictive Systems (CPS) to accurately estimate uncertainty in a suite of machine learning (ML)-based radio metric models [1] as well as in a 2-D map-based ML path loss model [2]. Utilizing diverse difficulty estimators, we construct 95% confidence prediction intervals (PIs) that are statistically robust. Our experiments demonstrate that CPS models, trained on Toronto datasets, generalize effectively to other cities such as Vancouver and Montreal, maintaining high coverage and reliability. Furthermore, the employed difficulty estimators identify challenging samples, leading to measurable reductions in RMSE as dataset difficulty decreases. These findings highlight the effectiveness of scalable and reliable uncertainty estimation through CPS in wireless network modeling, offering important potential insights for network planning, operations, and spectrum management.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。