The design and deployment of fifth-generation (5G) wireless networks pose significant challenges due to the increasing number of wireless devices. Path loss has a landmark importance in network performance optimization, and accurate prediction of the path loss, which characterizes the attenuation of signal power during transmission, is critical for effective network planning, coverage estimation, and optimization. In this sense, we utilize machine learning (ML) methods, which overcome conventional path loss prediction models drawbacks, for path loss prediction in a 5G network system to facilitate more accurate network planning, resource optimization, and performance improvement in wireless communication systems. To this end, we utilize a novel approach, nested cross validation scheme, with ML to prevent overfitting, thereby getting better generalization error and stable results for ML deployment. First, we acquire a publicly available dataset obtained through a comprehensive measurement campaign conducted in an urban macro-cell scenario located in Beijing, China. The dataset includes crucial information such as longitude, latitude, elevation, altitude, clutter height, and distance, which are utilized as essential features to predict the path loss in the 5G network system. We deploy Support Vector Regression (SVR), CatBoost Regression (CBR), eXtreme Gradient Boosting Regression (XGBR), Artificial Neural Network (ANN), and Random Forest (RF) methods to predict the path loss, and compare the prediction results in terms of Mean Absolute Error (MAE) and Mean Square Error (MSE). As per obtained results, XGBR outperforms the rest of the methods. It outperforms CBR with a slight performance differences by 0.4 % and 1 % in terms of MAE and MSE metrics, respectively. On the other hand, it outperforms the rest of the methods with clear performance differences.

翻译：第五代（5G）无线网络的设计与部署因无线设备数量的持续增长而面临重大挑战。路径损耗在网络性能优化中具有标志性意义，准确预测表征信号功率衰减特性的路径损耗对于有效的网络规划、覆盖估计和优化至关重要。为此，我们采用能克服传统路径损耗预测模型缺陷的机器学习（ML）方法，在5G网络系统中进行路径损耗预测，以促进无线通信系统更精确的网络规划、资源优化和性能提升。我们创新性地将嵌套交叉验证方案与ML相结合，通过防止过拟合获得更优的泛化误差和稳定的ML部署结果。首先，我们获取了通过在北京城区宏蜂窝场景中开展的综合测量实验得到的公开数据集。该数据集包含经度、纬度、高程、海拔、杂波高度和距离等关键信息，这些信息被用作预测5G网络系统路径损耗的核心特征。我们部署支持向量回归（SVR）、CatBoost回归（CBR）、极端梯度提升回归（XGBR）、人工神经网络（ANN）和随机森林（RF）方法进行路径损耗预测，并通过平均绝对误差（MAE）和均方误差（MSE）指标比较预测结果。实验结果显示，XGBR方法优于其他方法：在MAE和MSE指标上分别以0.4%和1%的微弱优势优于CBR，同时与其他方法相比呈现显著性能优势。