A Robust Machine Learning Approach for Path Loss Prediction in 5G
  Networks with Nested Cross Validation
Ibrahim Yazıcı Emre Gures
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.