Visible to the public Adaptive Root Cause Analysis for Self-Healing in 5G Networks

TitleAdaptive Root Cause Analysis for Self-Healing in 5G Networks
Publication TypeConference Paper
Year of Publication2017
AuthorsMfula, H., Nurminen, J. K.
Conference Name2017 International Conference on High Performance Computing Simulation (HPCS)
Date Publishedjul
Keywords5G, 5G mobile communication, 5G networks, adaptive learning, adaptive root cause analysis, Adaptive systems, ARCA, automated evidence based RCA, automated fault detection, Bayes methods, Bayesian network theory, belief networks, Cellular networks, composability, domain knowledge reuse, fault detection, fault diagnosis, fault tolerant computing, Incomplete Data, learning (artificial intelligence), Long Term Evolution, LTE Advanced, LTE-A, Manuals, network data, network experts, pattern classification, probabilistic Bayesian classifier, probability combinations, probable root cause, pubcrawl, quality of service, RCA process, resilience, Resiliency, return on investment, ROI, Root cause analysis, self-healing, self-healing networks, self-organising feature maps, self-organizing network, SH, SON based solutions, stratified synthesized data, telecommunication computing

Root cause analysis (RCA) is a common and recurring task performed by operators of cellular networks. It is done mainly to keep customers satisfied with the quality of offered services and to maximize return on investment (ROI) by minimizing and where possible eliminating the root causes of faults in cellular networks. Currently, the actual detection and diagnosis of faults or potential faults is still a manual and slow process often carried out by network experts who manually analyze and correlate various pieces of network data such as, alarms, call traces, configuration management (CM) and key performance indicator (KPI) data in order to come up with the most probable root cause of a given network fault. In this paper, we propose an automated fault detection and diagnosis solution called adaptive root cause analysis (ARCA). The solution uses measurements and other network data together with Bayesian network theory to perform automated evidence based RCA. Compared to the current common practice, our solution is faster due to automation of the entire RCA process. The solution is also cheaper because it needs fewer or no personnel in order to operate and it improves efficiency through domain knowledge reuse during adaptive learning. As it uses a probabilistic Bayesian classifier, it can work with incomplete data and it can handle large datasets with complex probability combinations. Experimental results from stratified synthesized data affirmatively validate the feasibility of using such a solution as a key part of self-healing (SH) especially in emerging self-organizing network (SON) based solutions in LTE Advanced (LTE-A) and 5G.

Citation Keymfula_adaptive_2017