基于3D电子地图和信道实测数据的市区路径损耗机器学习模型研究

随着5G移动通信系统的发展部署以及网络性能的优化，高精度和低复杂度的路径损耗预测模型尤为重要。该文针对大型城市场景，使用目前5G热点频段700 MHz, 2.4 GHz, 3.5 GHz的实测数据，将收发端位置、3维距离、相对余隙、建筑物密度、平均高度等作为环境特征，建立了基于3D电子地图的机器学习路径损耗预测模型，结果表明在复杂城市环境下，该文方法因其预测精度高而优于传统的基于收发端距离的路径损耗模型。另外，该文提出了基于频率迁移学习的路径损耗预测模型，并用均方误差、平均绝对百分比误差、均方根误差、决定系数等指标对其性能进行评估。该文方法可以解决建筑物遮挡严重的复杂城市环境以及在无大量测试数据的路径损耗预测问题，精确地预测城市环境中视距非视距混合信道的路径损耗值。

Research on Urban Path Loss Model by Machine Learning Based on 3D Electronic Maps and Channel Measurements

With the development of 5G mobile communication systems and the optimization of network performance, high-precision and low-complexity path loss prediction models become more important. This paper combined the location of the receiver and transmitter, three-dimensional distance, relative clearance, building density, average height and other environmental characteristics, a machine learning path loss prediction model based on 3D electronic maps is established. And the current 5G hot spot frequency bands data at 700 MHz, 2.4 GHz, 3.5 GHz which measured in large-scale urban scenes are used for training and verification. Results show that the method in this paper has higher prediction accuracy in a complex urban environment, and it is better than the traditional model which is based on the distance between the transmitter and receiver. In addition, a machine learning path loss prediction model based on frequency transfer is also proposed, and the performance is evaluated by using indicators like mean square error, average absolute percentage error, root mean square error, coefficient of determination, etc. The proposed methods can solve the problem of path loss prediction in a complex urban environment with severe building obstruction and without a large amount of test data. Moreover, it can accurately predict the path loss value of the mixed channel consist of line-of-sight and non-line-of-sight in the urban environment.