5G Network Slicing with K-Means Clustering | AI in Telecom | Telecom | Network Slicing

Why It Matters

Automating 5G slice classification with K‑means reduces labeling effort and enables real‑time network resource orchestration, directly impacting operator efficiency and service quality.

Key Takeaways

• Import and clean 5,000‑sample 5G slice dataset before modeling.
• Use IQR to remove outliers and ensure data quality.
• Apply scaling to balance throughput, latency, and jitter features.
• Determine optimal K=3 via elbow method and silhouette score.
• Map cluster labels to EMBB, URLLC, and mMTC using majority voting.

Summary

The video walks through a Python notebook that uses K‑means clustering to separate 5G network slices—enhanced mobile broadband (eMBB), ultra‑reliable low‑latency communications (URLLC) and massive machine‑type communications (mMTC)—from a 5,000‑sample dataset.

After importing pandas, NumPy and scikit‑learn, the presenter cleans the data, removes duplicates, checks for missing values, applies an IQR filter to drop outliers, and visualizes distributions with histograms, box‑plots and correlation matrices. The features (throughput, latency, packet loss, signal strength, UE count, jitter) are scaled, then split 80/20 with stratification on the hidden slice label.

Cluster count is chosen by plotting inertia (elbow method) and confirming the silhouette score, both indicating three clusters as optimal. The model runs K‑means with 300 iterations and random_state 42, producing labels 0, 1, 2. A majority‑vote mapping aligns each label to its true slice type, e.g., cluster 0 corresponds to mMTC, cluster 1 to eMBB, and cluster 2 to URLLC, and the same mapping is applied to the test set for validation.

By demonstrating an unsupervised pipeline that reliably recovers slice categories, the tutorial shows telecom operators how to automate slice identification and resource allocation without extensive labeled data, accelerating 5G service rollout and network optimization.

Original Description

🚀 Want to understand how AI is transforming 5G networks?

In this video, we explore how K-Means Clustering, a popular Machine Learning algorithm, can be used to enable 5G Network Slicing — one of the most powerful features of 5G technology.

🔍 What you’ll learn:

What is 5G Network Slicing?

Types of slices: eMBB, URLLC, mMTC

Basics of K-Means Clustering

How clustering helps dynamically group users/services

Real-world telecom use case with AI

💡 Why this matters:

As 5G networks evolve, intelligent automation using AI/ML is becoming critical for efficient resource allocation, QoS management, and service differentiation.

📊 Perfect for:

Telecom Engineers

Data Science Enthusiasts

5G/6G Learners

AI in Telecom Professionals

🛠️ Tools & Concepts Covered:

Machine Learning (K-Means Clustering)

Telecom KPIs & QoS

5G Architecture & Use Cases

👇 Don’t forget to:

Visit www.TelcoLearn.com

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#5G #NetworkSlicing #MachineLearning #AIinTelecom #KMeans #6G #TelcoAI