# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 3 Proposed System
### Inference/Classification  
The objective of the inference stage is to classify the status of the network to establish the abnormality of its behavior, as well as to define the posterior actions to be taken on it.  
In this respect, different techniques can be used based on the available indicators. For this work, supervised mechanisms are considered. These need to be trained based on indicators coming from the network or the FE process and the labels containing information on the status of the network when they were obtained.  
Other possibilities in the use of inference mechanisms are the analysis based purely on individual values (where the value of each indicator that is associated with a measurement is analyzed independently) or by means of time series of the indicators with respect to past values of it. In our case, and, given the dynamic conditions of the Ultra-Dense Network (UDN) environment, time-dependent characteristics of the metrics are too much affected by the highly variable user occupancy and demand. Therefore, only mechanisms omitting the temporal characteristics of the metrics are considered.  
Once the proper indicators have been selected and/or generated, the inference block is dedicated to its application in failure management. In this way, its first objective is to classify the status of the network to establish the abnormality of its behavior (detection) and identify the specific failure behind the issue (diagnosis). In this respect, different techniques can be used based on the available indicators for both detection and diagnosis [19; 20; 21]. The output of the classifier would then be used to advise human staff or to directly guide compensation and/or recovery actions in the network.  
The CAFAIM framework focuses on the provision of the metrics combining localization and cellular data, as well as in the feature engineering of such metrics to make the inference/classification phases reliable and efficient. Hence, for the classification stage, CAFAIM is algorithm-agnostic and its inference block can accommodate different multi-class classifiers for the diagnosis process. For the present work, and in order to have a broad view of the capabilities of the approach to improve the inference stage, the inference block features three key ML classification algorithms: k-nearest-neighbors (kNN) [22], discriminant analysis classification (DISC) [23], and error/correcting output codes classification (ECOC) [24]. kNN is a non-parametric classifier, which establishes a category for each class in the feature space of the data and classifies an object in one of the categories by a plurality vote of its \"k\" nearest neighbors. DISC is a classifier that assumes the different classes are based on Gaussian distributions. During training, this classifier fits a Gaussian distribution for each class and, afterwards, every new object is classified in the class, which minimizes the error when being fitted in the distribution. Finally, ECOC is a multi-variate mechanism based on binary classifiers: contrary to other similar methods, ECOC establishes a binary classifier for each class instead of using a binary classifier to compare each class with the rest, predicting the class from within an over-determined space of solutions.