用众包数据和环境特征预测4G信号，精度高且适应复杂城市场景。
Machine Learning for Modeling Wireless Radio Metrics with Crowdsourced Data and Local Environment Features
- 融合众包数据与本地环境特征建模无线信号指标。
- 在多城市超30万数据点上，RSRP误差低于11.7dB，RSRQ误差低于3.23dB。
- 适合网络规划与服务质量优化，尤其适用于复杂城市环境。
本文提出一套机器学习模型CRC-ML-Radio Metrics，用于建模4G环境下RSRP、RSRQ和RSSI无线信号指标。该模型结合众包数据与本地环境特征，在多伦多、蒙特利尔和温哥华地区超过30万条数据上进行评估，实现了9.76至11.69 dB的RSRP RMSE，2.90至3.23 dB的RSRQ RMSE，以及9.50至10.36 dB的RSSI RMSE。结果表明模型具备强鲁棒性与适应性，可有效支持复杂加拿大城市环境中精准的网络规划与服务质量优化。
原文摘要 · Abstract (English)
This paper presents a suite of machine learning models, CRC-ML-Radio Metrics, designed for modeling RSRP, RSRQ, and RSSI wireless radio metrics in 4G environments. These models utilize crowdsourced data with local environmental features to enhance prediction accuracy across both indoor at elevation and outdoor urban settings. They achieve RMSE performance of 9.76 to 11.69 dB for RSRP, 2.90 to 3.23 dB for RSRQ, and 9.50 to 10.36 dB for RSSI, evaluated on over 300,000 data points in the Toronto, Montreal, and Vancouver areas. These results demonstrate the robustness and adaptability of the models, supporting precise network planning and quality of service optimization in complex Canadian urban environments.
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