Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks

Summary

This paper talks about a lightweight system that uses Large Language Models to find the main reasons why problems happen in 5G wireless networks. It uses a special dataset called TeleLogs and trains the model in two steps to make it better at understanding and explaining network issues.

What's the problem?

The problem is that 5G networks are very complex and problems like slow internet or dropped connections can have many causes, but it’s hard to figure out the exact reason quickly. Existing tools either aren’t accurate enough or don’t explain their decisions well.

What's the solution?

The solution was to build a framework that fine-tunes large language models to reason about network data by learning from the TeleLogs dataset, which simulates real-world 5G problems. The training happens in two stages to make the model both more accurate and better at explaining why it thinks a certain problem happened.

Why it matters?

This matters because finding and fixing network problems fast helps keep 5G service reliable, which is important for things like streaming, smart cities, and self-driving cars. By using AI that can reason and explain itself, network engineers can solve issues more efficiently.