Abstract
In this chapter, we present a Cell Outage Detection (COD) framework for Heterogeneous Networks (HetNets) with split control and data planes. COD is a pre-requisite to trigger fully automated self-healing recovery actions following cell outages or network failures not only to ensure reliable recovery of services but also to significantly minimize wastage of energy. To cope with the idiosyncrasies of both the data and control planes, our proposed framework incorporates control COD and data COD mechanisms. The control COD leverage the relatively larger number of UEs in the control cell to gather large scale Minimize Drive Testing (MDT) reports data. These measurements are further pre-processed using multidimensional scaling method and are employed together with state-of-the art machine learning algorithms to detect and localize anomalous network behaviour. On the other hand, for data cells COD, we propose a heuristic Grey-Prediction based approach, which can work with the small number of UEs in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity, by receiving a periodic update of the Received Signal Reference Power (RSRP) statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the fourier series of residual error that is inherent to grey prediction model. We validate and demonstrate the effectiveness of our proposed solution for detecting cell outages in both data and control planes via performing network simulations under various operational settings.