Comparative analysis of ML algorithms for 5G coverage prediction review

Summary

• Model performance in 5G coverage prediction is primarily determined by the alignment between data characteristics, feature design, and model inductive bias, rather than by model complexity alone.
• Using real-world 5G NR drive-test data with physics-informed numerical features, this study demonstrates that Random Forest can achieve SOTA performance, outperforming more complex models such as XGBoost and deep neural networks.
• The results highlight the continued importance of domain-informed feature engineering and show that deep learning becomes advantageous only when the data representation and scale justify its use.

Introduction

• Coverage prediction in 5G networks is a core component of network planning, optimization, and resource allocation.
• Conventional propagation and path loss models are limited in their ability to accurately capture the complexity of dense urban environments and the unique characteristics of 5G systems.
• Machine Learning and Deep Learning have emerged as promising alternatives, as they can model complex non-linear relationships across multiple parameters.
• However, prior studies typically suffer from several limitations:
  • Most focus on 4G networks or rely on a limited set of input features.
  • Comparisons across a wide range of algorithms are often insufficient.
  • Systematic analyses of feature importance are largely lacking.

The objectives of this study are to:

• Conduct a comprehensive comparison of multiple ML and DL algorithms using a unified dataset.
• Identify dominant feature parameters that significantly influence 5G coverage prediction.
• Demonstrate performance improvements over previously reported methods.

Methods

Data Collection

• Real-world 5G NR drive test measurements conducted in Bandung, Indonesia (Batununggal area).
• Approximately 1,500 SS-RSRP samples collected.
• Deployment includes 10 gNodeBs, each configured with three sectors.
• Measurement vehicle speed maintained below 30 km/h to minimize fast fading effects.

Input Features (10 Total)

• 2D Distance between Transmitter and Receiver
• Frequency
• Transmitter Tilt Angle
• Transmitter Azimuth Angle
• Altitude
• Elevation Angle
• Azimuth Offset Angle
• Tilting Offset Angle
• Horizontal Distance of Receiver from Transmitter Antenna Boresight
• Vertical Distance of Receiver from Transmitter Antenna Boresight

Algorithms

Machine Learning (Classification-based):

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Random Forest
• Support Vector Machine (SVM)
• XGBoost
• LightGBM
• AdaBoost
• Bayesian Network Classifier

Deep Learning:

• Multi-Layer Perceptron (MLP)
• Long Short-Term Memory (LSTM)
• Convolutional Neural Network (CNN)

Training and Validation

• Experiments conducted using Google Colab.
• 10-fold cross-validation applied for all models.
• Hyperparameter optimization performed only on the best-performing models.

Evaluation Metrics

• Regression Metrics: RMSE, MAE, R²
• Classification Metrics: Accuracy, Precision, Recall, F1-score

Results

Machine Learning

Random Forest:

• RMSE = 1.14 dB
• MAE = 0.12
• R² = 0.97
• Accuracy / Precision / Recall / F1-score ≈ 98.4%

Deep Learning

Convolutional Neural Network (CNN):

• RMSE = 0.289

• MAE = 0.289

• R² = 0.78

• Accuracy = 75%

• Precision = 85.6%

• Recall = 87.8%

• F1-score = 89.9%

• MLP and LSTM exhibit inferior performance compared to CNN.

Feature Importance

• The 2D Transmitter–Receiver Distance is identified as the most dominant feature across all algorithms.
• Incorporating horizontal and vertical distances from the antenna boresight significantly improves prediction accuracy.

Comparison with Previous Studies

• Both Random Forest and CNN achieve lower RMSE values compared to prior studies.
• Random Forest, in particular, demonstrates state-of-the-art performance relative to existing 4G and 5G coverage prediction research.

Discussion

• Random Forest
  • Highly effective for small-to-medium-sized datasets with numerical features.
  • Offers strong interpretability and robust performance stability.
• Convolutional Neural Network
  • Well-suited for grid-based or spatial data representations.
  • Shows greater potential when image-based or satellite-derived features are incorporated.
  • In this study, CNN was applied by transforming numerical features into a matrix-like structure.
• The results empirically demonstrate that feature design and selection can be more critical than the choice of learning algorithm itself.