Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks

A Hybrid Deep Learning-Based (HYDRA) Framework for Multifault Diagnosis Using Sparse MDT Reports

“…We compare the performance of HYDRA against the state-of-the-art ML methods used for detection and diagnosis of outages in the literature. These include SVM [12], [46], [47], RF [24], [25], standalone XGBoost and standalone CNN [24]. We evaluate HYDRA with different UE densities to analyze its efficacy in realistic settings (i.e., robustness to sparsity of MDT reports in a cell/area).…”

“…The most relevant to this work are the studies presented in [24]- [26]. The researchers in [24] and [25] present a fault diagnosis solution using neuromorphic AI and classical ML methods, respectively. They use MDT reports to generate radio environment maps (REMs).…”

“…Also, both these studies consider faults in a single BS, i.e., they assume a single fault at a time. Hence, while offering a promising first set of results on fault diagnosis using only MDT data in a multi-BS network, the aforementioned assumptions in [24], [25] render these solutions unsuitable for real deployment. In [26] authors present a deep learning (DL)-based solution for the multifault diagnosis in one BS.…”

“…This challenge is by passed in the literature by often assuming availability of complete and noise free REMs. Therefore, classic models such as SVM [12], RF [24], [25] and CNN [8] are often used and observed to yield adequate performance in the literature. Our analysis shows that these classic models do not attain adequate performance when realistically sparse MDT data is used to generate REMs.…”

“…To the best of our knowledge image inpainting is not used in cellular domain because there are just few published works, where MDT reports are used in the form of REMs. The available literature which considers REM-based outage diagnosis [24], [25], does not consider sparsity. This study is the first to investigate impact of data sparsity and provide a detailed performance comparison of state-of-the-art-image inpainting techniques to address the practical problem of sparsity in MDT data and REMs in Section IV-A.…”