HabibKiz/4g-congestion-detection-prediction
Domaine:
digital infrastructureType de record:
project
Créateur:
Hab
Hôte:
Hybrid ML pipeline for 4G network congestion detection and forecasting. Uses HDBSCAN clustering to segment cell behaviour and XGBoost to predict KPI trajectories 1-7 days ahead. Built on live Orange Mali RAN data. MSc thesis, University of Middlesex Dubai / Sofrecom (Orange Group).
# Intelligent Network Monitoring: Clustering-based Detection and Predictive Modelling of Congestion in 4G Networks
**MSc Thesis · University of Middlesex Dubai · 2025**
**Author:** Habib Kizamou · Internship at Sofrecom (Orange Group), Rabat
---
## Overview
Telecom networks generate massive streams of KPI data — but turning that data into early warnings operators can actually act on is the real challenge. This project tackles exactly that, using a **hybrid ML pipeline** combining unsupervised clustering and supervised forecasting on live 4G radio access network data from **Orange Mali**.
The pipeline moves from raw operational exports to two operational outputs:
1. **Clustering** — segment network cells by behaviour to surface congestion-risk cohorts
2. **Forecasting** — predict KPI trajectories 1–7 days ahead to enable proactive intervention
---
## Results at a Glance
| Task | Best Model | Key Metric |
|------|-----------|------------|
| Cell segmentation (11 KPIs, 1M+ obs) | HDBSCAN | Silhouette: **0.69–0.70** |
| Short-term forecasting (1–7 day) | XGBoost | **30–60% lower MAE** vs Prophet |
| Synthetic Busy Hour generation | XGBoost Tweedie | 1 month → **6 months** coverage |
---
## Dataset
Data was extracted from **Orange Mali's 4G Radio Access Network** via Sofrecom's PRS interface — live operational KPI exports across four temporal granularities:
| Granularity | Rows |
|------------|------|
| Weekly | ~637K+ (combined) |
| Daily | |
| Hourly | |
| Busy Hour (BH) | |
**11 KPIs tracked:** accessibility, retainability, mobility, traffic volume, throughput, and user experience metrics.
> ⚠️ **Note:** Raw data is proprietary to Orange Group / Sofrecom and is not included in this repository. Notebooks use anonymised or synthetic data where applicable.
---
## Repository Structure
```
4g-congestion-detection-prediction/
│
├── notebooks/
│ ├── 01_data_preparation/ # Cleaning, standardisation, synthetic BH creation
│ ├── 02_eda/ …
Similaires
LACMAGO05/Intelligent-traffic-analysis-and-prediction-congestion-systemCityPulse: Real-Time Traffic Data Analytics and Congestion PredictionAn AI-driven Approach to Traffic Congestion Prediction in Port Harcourt City Using Naïve BayesTOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATATOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATA نحو طريقة فعالة للتنبؤ بازدحام المرور تعتمد على الشبكات العصبية وبيانات نظام تحديد المواقع العالمي الكبيرة TOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATA TOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATA TOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATAPrediction of Missing Values of Critical 4G LTE Parameters Using Experimental Dataset Derived from a Dense Urban Environment