How to Classify WiFi Problems with Machine Learning

WiFi problems are among the most frustrating issues users face — slow speeds, dropped connections, or mysterious lag that no restart seems to fix.
But what if your router could automatically diagnose what’s wrong, without needing a network expert?

That’s exactly what our research set out to achieve in the paper
On the Employment of Machine Learning Techniques for Troubleshooting WiFi Networks,
presented at IEEE CCNC 2019.

The Problem

As WiFi networks grow denser — with laptops, IoT devices, smart TVs, and microwaves all sharing the same spectrum — diagnosing poor performance becomes extremely complex.

Even experts struggle to pinpoint whether the issue comes from:

• Contention (too many WiFi devices competing for the same channel),
• Low signal-to-noise ratio (SNR),
• Non-WiFi interference (like Bluetooth or microwave ovens),
• Hidden terminal problems, or
• Capture effect (when one signal overpowers another).

Traditional troubleshooting requires specialized equipment and deep knowledge of the 802.11 protocol — something the average user doesn’t have.

Approach

We designed a machine learning framework that allows any low-cost WiFi access point (AP) to become “intelligent.”
By analyzing data already available from the AP’s network drivers (like transmission success rates and channel access frequency), the system can automatically classify the root cause of a connection issue.

Two detection modes were developed:

1. Passive Mode – Gathers performance metrics without sending any extra network traffic.
   → Low overhead, ideal for everyday monitoring.

2. Active Mode – Uses controlled test packets (probing) to collect richer data.
   → Slightly more overhead, but achieves higher accuracy.

The Machine Learning Models

We evaluated four popular classification algorithms:

• Decision Trees
• Random Forests
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)

Each algorithm was trained and fine-tuned on a dataset of over 5,000 labeled samples, representing real-world WiFi problems reproduced in a controlled testbed.

The features used were two simple but powerful metrics:

• Normalized Channel Access (NCA): How often a device gets to transmit.
• Frame Delivery Ratio (FDR): The ratio of successful transmissions.

By combining these with information about the modulation scheme (MCS) used, the models learned to differentiate between all five WiFi pathologies.

Results

After fine-tuning and cross-validation, the results were remarkable:

The KNN model achieved near-perfect classification accuracy — 99% for active probing and 95% for passive detection — with minimal computational requirements.

Why It Matters

This work demonstrates that:

• Machine learning can make WiFi networks self-diagnosing.
• Commodity routers can be transformed into intelligent monitoring tools.
• The approach is affordable, deployable, and scalable for both home and enterprise networks.

Future extensions include multi-label detection, allowing the system to recognize multiple overlapping issues simultaneously — a step toward fully autonomous WiFi troubleshooting.

Reference

Ilias Syrigos, Nikos Sakellariou, Stratos Keranidis, and Thanasis Korakis,
“On the Employment of Machine Learning Techniques for Troubleshooting WiFi Networks,”
IEEE CCNC, 2019.
Read the full paper on ResearchGate

Summary:
Our model allows routers to detect and explain WiFi performance issues automatically using simple ML models — without expensive hardware or expert intervention.
It’s a practical example of how AI can improve everyday connectivity.