Root Cause Diagnosis; Cloud VR; Anomaly Detection; Contrastive Learning; Time Series Classification
Abstract :
[en] Wi-Fi networks are widely used for modern connectivity but
remain vulnerable to impairments such as bandwidth fluctuations, interference, packet loss and latency spikes. These challenges make it difficult to support latency-sensitive applications like Cloud Virtual Reality (Cloud VR), which offloads intensive computation to remote servers
to reduce local hardware requirements but demands high throughput
and ultra-low latency. Consequently, Wi-Fi network degradations can
severely impact the Quality of Experience (QoE) of such applications.
Traditional Root Cause Diagnosis (RCD) approaches rely on expertdefined rules or supervised ML (Machine Learning) models that require
extensive labeled datasets. This dependence on manual labeling makes
them costly, time-consuming, and impractical for real-world Wi-Fi diagnostics.
To overcome these limitations, we introduce RAID (Root cause Anomaly
Identification and Diagnosis), a two-stage ML framework that diagnoses
Wi-Fi performance issues using time series KPIs collected directly from
the Wi-Fi access point, with Cloud VR serving as a use case. RAID combines contrastive learning-based anomaly detection with a lightweight
classifier to categorize network impairments. We evaluate RAID, with
a real-world Cloud VR use case, in a testbed using NVIDIA CloudXR
and a Meta Quest 2, collecting Wi-Fi performance metrics on the access point, under controlled conditions. Results demonstrate that RAID
outperforms existing RCD methods, achieving high accuracy even with
minimal labeled data. Compared to conventional supervised and selfsupervised time series models, RAID offers a scalable, real-time solution
with a good trade-off between training efficiency and inference speed,
making it well-suited for practical deployment in dynamic Wi-Fi network environments.
Disciplines :
Computer science
Author, co-author :
KY, Joel ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal ; INRIA - Institut National de Recherche en Informatique et en Automatique > LORIA > Resist
Mathieu, Bertrand; Orange Innovation
Lahmadi, Abdelkader; INRIA - Institut National de Recherche en Informatique et en Automatique
Wang, Minqi; Orange Innovation
Marrot, Nicolas; Orange Innovation
Boutaba, Raouf; University of Waterloo > David R. Cheriton School of Computer Science
External co-authors :
yes
Language :
English
Title :
RAID: Root Cause Anomaly Identification and Diagnosis
Publication date :
04 October 2025
Event name :
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD
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