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.

翻译：本文提出了一套机器学习模型CRC-ML-Radio Metrics，用于在4G环境中对RSRP、RSRQ和RSSI无线射频指标进行建模。这些模型利用包含局部环境特征的众包数据，以提高在室内高层及室外城市场景下的预测精度。在覆盖多伦多、蒙特利尔和温哥华地区超过30万个数据点的评估中，模型对RSRP的RMSE性能达到9.76至11.69 dB，对RSRQ达到2.90至3.23 dB，对RSSI达到9.50至10.36 dB。这些结果证明了模型在复杂的加拿大城市环境中的鲁棒性与适应性，可为精准的网络规划和服务质量优化提供支持。