Multi-Level Association Rule Mining for Wireless Network Time Series Data

Key performance indicators(KPIs) are of great significance in the monitoring of wireless network service quality. The network service quality can be improved by adjusting relevant configuration parameters(CPs) of the base station. However, there are numerous CPs and different cells may affect each other, which bring great challenges to the association analysis of wireless network data. In this paper, we propose an adjustable multi-level association rule mining framework, which can quantitatively mine association rules at each level with environmental information, including engineering parameters and performance management(PMs), and it has interpretability at each level. Specifically, We first cluster similar cells, then quantify KPIs and CPs, and integrate expert knowledge into the association rule mining model, which improve the robustness of the model. The experimental results in real world dataset prove the effectiveness of our method.

AI Empowered Net-RCA for 6G

6G is envisioned to offer higher data rate, improved reliability, ubiquitous AI services, and support massive scale of connected devices. As a consequence, 6G will be much more complex than its predecessors. The growth of the system scale and complexity as well as the coexistence with the legacy networks and the diversified service requirements will inevitably incur huge maintenance cost and efforts for future 6G networks. Network Root Cause Analysis (Net-RCA) plays a critical role in identifying root causes of network faults. In this article, we first give an introduction about the envisioned 6G networks. Next, we discuss the challenges and potential solutions of 6G network operation and management, and comprehensively survey existing RCA methods. Then we propose an artificial intelligence (AI)-empowered Net-RCA framework for 6G. Performance comparisons on both synthetic and real-world network data are carried out to demonstrate that the proposed method outperforms the existing method considerably.

Task-aware Similarity Learning for Event-triggered Time Series

Time series analysis has achieved great success in diverse applications such as network security, environmental monitoring, and medical informatics. Learning similarities among different time series is a crucial problem since it serves as the foundation for downstream analysis such as clustering and anomaly detection. It often remains unclear what kind of distance metric is suitable for similarity learning due to the complex temporal dynamics of the time series generated from event-triggered sensing, which is common in diverse applications, including automated driving, interactive healthcare, and smart home automation. The overarching goal of this paper is to develop an unsupervised learning framework that is capable of learning task-aware similarities among unlabeled event-triggered time series. From the machine learning vantage point, the proposed framework harnesses the power of both hierarchical multi-scale sequence autoencoders and Gaussian Mixture Model (GMM) to effectively learn the low-dimensional representations from the time series. Finally, the obtained similarity measure can be easily visualized for explaining. The proposed framework aspires to offer a stepping stone that gives rise to a systematic approach to model and learn similarities among a multitude of event-triggered time series. Through extensive qualitative and quantitative experiments, it is revealed that the proposed method outperforms state-of-the-art methods considerably.