Data mining framework for random access failure detection in LTE networks

Abstract

Sleeping cell problem is a particular type of cell degradation. There are various software and hardware reasons that might cause such kind of cell outage. In this study a cell becomes sleeping because of Random Access Channel (RACH) failure. This kind of network problem can appear due to misconfiguration, excessive load or software/firmware problem at the Base Station (BS). In practice such failure might cause network performance degradation, which is hardly traceable by an operator. In this paper we present a data mining based framework for the detection of problematic cells. In its core is the analysis of event sequences reported by a User Equipment (UE) to a serving BS. The choice of N in N-gram feature selection algorithm is considered, because of its significant impact on computational efficiency. Moreover, qualitative and heuristic performance metrics have been developed to assess the performance of the proposed detection algorithm. Sleeping cell detection framework is verified by means of dynamic LTE (Long-Term Evolution) system simulator, using Minimization of Drive Testing (MDT) functionality. It is shown that sleeping cell can be determined with very high reliability even using 1-gram algorithm.