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Comparative analysis of ML algorithms for 5G coverage prediction review

· 3 min read
Gracefullight
Gracefullight
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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.