Accurate outdoor positioning in cellular networks is hindered by sparse, heterogeneous measurement collections and the high cost of exhaustive site surveys. This paper introduces a lightweight, modular mobile data augmentation framework designed to enhance multi-cell fingerprinting-based positioning using operator-collected minimization of drive test (MDT) records. The proposed approach decouples spatial and radio-feature synthesis: kernel density estimation (KDE) models the empirical spatial distribution to generate geographically coherent synthetic locations, while a k-nearest-neighbor (KNN)-based block produces augmented per-cell radio fingerprints. The architecture is intentionally training-free, interpretable, and suitable for distributed or on-premise operator deployments, supporting privacy-aware workflows. We both validate each augmentation module independently and assess its end-to-end impact on fingerprinting-based positioning using a real-world MDT dataset provided by an Italian mobile network operator across diverse urban and peri-urban scenarios. Results show that the proposed KDE-KNN augmentation consistently improves positioning performance with respect to state-of-the-art approaches, reducing the median positioning error by up to 30% in the most sparsely sampled or structurally complex regions. We also observe region-dependent saturation effects, which emerge most rapidly in scenarios with high user density where the information gain from additional synthetic samples quickly diminishes. Overall, the framework offers a practical, low-complexity path to enhance operator positioning services using existing mobile data traces.

翻译：蜂窝网络中精确的室外定位受到稀疏、异构的测量数据收集以及详尽站点勘测的高成本所制约。本文提出一种轻量级、模块化的移动数据增强框架，旨在利用运营商采集的最小化路测记录来增强多小区指纹定位性能。该方法将空间特征与无线电特征合成进行解耦：通过核密度估计对经验空间分布进行建模以生成地理连贯的合成位置，同时采用基于k近邻的模块生成增强的每小区无线电指纹。该架构采用无训练设计，具有可解释性，适用于分布式或本地化运营商部署，并支持隐私感知的工作流程。我们分别独立验证了各增强模块，并基于意大利移动网络运营商提供的涵盖多种城市及城郊场景的真实MDT数据集，评估了其对端到端指纹定位性能的影响。结果表明，所提出的KDE-KNN增强方法相较于现有先进方案能持续提升定位性能，在采样最稀疏或结构最复杂的区域中，中位数定位误差降低幅度最高可达30%。同时我们观察到区域依赖的饱和效应，这种现象在用户密度较高的场景中显现最快，此时额外合成样本带来的信息增益会迅速衰减。总体而言，该框架为利用现有移动数据轨迹增强运营商定位服务提供了一条实用且低复杂度的技术路径。