Complex Event Processing for Self-Optimizing Cellular Networks

Abstract

In a cellular network, signaling and data messages exchanged between network elements are an extremely valuable information for network optimization. The consideration of different types of information allows to improve the optimization results. However, the huge amount of information has made it very difficult for operators to process all the available information. To cope with this issue, in this paper, a methodology for processing cell and user connection traces to optimize a live cellular network is presented. The aim is to generate new performance indicators different from those supplied by manufacturers, taking advantage of the ability of complex event processing tools to correlate events of different nature. For illustrative purposes, an example of how a new performance indicator is created from real traces by complex event processing is given.