# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 3 Proposed System
### Indicators Generation
Assuming this, the need to specify which samples must be filtered in or out for each weight function is still to be defined. For this, the present work applies the concept of _Areas of Interest_ (AoIs): here, the different contextualized indicators are calculated as the statistics associated with the measurements coming from the UEs located at different AoIs. These AoIs define specific regions of the cell coverage from where obtaining differentiated statistics/indicators can highly improve classification performance for both detection and diagnosis. For example, the mean RSRP at the center of a cell can give us a better grasp of possible degradations in its transmitted power that the mean associated with the complete cell coverage, which makes it a better input for classification algorithms. Previous works on the integration or merging of context and network information have revolved around only using human defined integrating functions, specifically selected for their expected statistical value in the identification of specific failures or problems [19; 20]. However, this selection is limited by the experience of the human personnel. Given the novel introduction of context into the available metrics, this knowledge cannot go beyond guessing which metrics would be the most useful for the classification of the network status as this might lead to sub-optimal solutions. Moreover, where previous works only considered as AoIs the _center_ or the _edge_ of the cells, the present work highly extends the use of AoIs, implementing a completely autonomous algorithm for the generation of the metrics that are associated with each of the possible areas that can be the most affected by different failures. Here, the additional concepts of _influenced_ and _influencing_ areas is introduced. The area of any cell A influenced by another neighboring cell B is the part of cell A, where most likely cell B changes (e.g., an increase of transmitted power) might affect the UEs expected in a normal situation to be served by A. Accordingly, cell A influencing area in B would be that where changes in the A status would most likely impact cell B served users. From this, any superposition between different AoIs is also considered, e.g., the center of A influenced by B, the edge of B influenced by A, and so on. For the automatic generation of the limits of all these areas multiplicatively weighted Voronoi tessellations [10] are adopted. An example of estimated AoIs is presented in Figure 3, for the airport scenario to be considered in the evaluation section, and for the case where all the cells are expected to transmit with the same power, making the tessellation equal to a non-weighted Voronoi diagram, where the \"seeds\" of the tessellation are located at the BSs positions. From these, center areas are then generated as circles around the BSs and with a radius equal to a percentage of the minimum distance between the BS and the closest border of its estimated coverage. Additionally, the influenced areas of a given cell A (11 in the figure) are also estimated by multiplicatively weighted Voronoi tessellation, but without considering the position of the cell A as a seed of the tessellation. In this way, the areas of the neighboring cells also encompass cell A original coverage. The intersection of these neighbor coverage areas and the previously estimated coverage, center, and/or edge serve as the influenced areas of cell A for each of its neighbors. In a similar way, the influencing areas of cell A in its neighbors can be estimated by avoiding their positions as seeds of the tessellation. Figure 3: Area-based automatic indicators generation in CAFAIM, showing cell 11 proper, influenced, and influencing areas for an airport scenario.