Toward Realistic Wi-Fi Fault Diagnosis: A Multi-Modal Benchmark
This provides the first publicly available multi-modal dataset and benchmark for Wi-Fi fault diagnosis, addressing the lack of realistic evaluation in heterogeneous operational environments.
The authors built a real-world Wi-Fi testbed with automated fault injection, collected a multi-modal dataset of over 10,000 fault samples, and established a benchmark for Wi-Fi fault diagnosis. They found that existing approaches struggle with heterogeneous data and that LLM-based methods need better consistency with actual network conditions.
Intelligent network operation and maintenance systems in modern networks continuously generate large volumes of multi-modal operational data. However, Wi-Fi fault diagnosis under heterogeneous operational environments remains insufficiently understood. We build a real-world Wi-Fi testbed deployed in campus working environments with an automated fault injection system, and collect a multi-modal Wi-Fi fault dataset containing over 10,000 fault samples across diverse wireless scenarios. To the best of our knowledge, this is among the first publicly available datasets jointly capturing heterogeneous cross-layer operational observations for Wi-Fi fault diagnosis. Based on this dataset, we establish a unified benchmark spanning multiple diagnosis tasks, operational modalities, and representative diagnosis paradigms. Experimental results indicate that effectively leveraging heterogeneous operational data remains challenging for existing diagnosis approaches. We further evaluate emerging LLM-based approaches and develop a reasoningoriented evaluation framework to assess the consistency between generated diagnostic analyses and actual network conditions. Our findings suggest several important considerations for future multi-modal Wi-Fi diagnosis.