Cognitive self-healing system for future mobile networks

This paper introduces a framework and implementation of a cognitive self-healing system for fault detection and compensation in future mobile networks. Performance monitoring for failure identification is based on anomaly analysis, which is a combination of the nearest neighbor anomaly scoring and statistical profiling. Case-based reasoning algorithm is used for cognitive self-healing of the detected faulty cells. Validation environment is Long Term Evolution (LTE) mobile system simulated with Network Simulator 3 (ns-3) [1, 2]. Results demonstrate that cognitive approach is efficient for compensation of cell outages and is capable to improve network coverage. Anomaly analysis can be used for identification of network failures, and automation of performance management. Introduction of data mining and cognition to the future mobile networks, e.g. 5^th Generation (5G), is especially important as it allows to meet the strict requirements for robustness and enhanced performance.