# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 2 Context-Awareness and Its Impact in the Management of Cellular Systems  
Cellular management schemes have classically relied on network-related metrics, such as alarms, counters, key performance indicators (KPI)s [1] and sometimes, UE traces, as represented in Figure 1. These are used as the inputs for the classification and controller systems that are dedicated to establishing/classifying the status of the network (e.g., its performance or whether it is under a specific failure) as well as to define the actions to be taken in order to compensate network service degradation/problems (e.g., excessive dropped calls), recover from specific failures, and/or optimize the network behavior [1]. The generation of these metrics relies on network-related measurements and events coming from UEs, BSs/cells, or other network elements. These measurements/events are aggregated into the different metrics, for example, by their count for a specific period (e.g., dropped calls each hour). Other metrics are based on the combination of multiple counters and/or statistics at cell-level, e.g., mean Reference Signal Received Power (RSRP)), mean Reference Signal Received Quality (RSRQ), number of handovers per period of one hour, etc. This leads to a huge number of metrics: only taking into account metrics based on network measurements and events that are related to the UEs served by a cell, the total number of indicators for a specific scenario is a function of the number of measurable values (e.g., RSRP, RSRQ, drops, throughput...), multiplied by the number of calculated statistics (mean, Xth percentile) and by the number of cells. This number is then incremented by the KPIs generated from the counters as well as the version of the same counters and KPIs but with different periodicities and for different radio access technologies. Such metrics are  
Figure 1: Classic approach for cellular network management. then used as inputs for inference/classification of the status of the network performance and the most likely failure behind it, as shown in Figure 1. Such identification of the network status serves to define specific actions to compensate the issue and/or recover or optimize the network. The metrics that are used to feed the inference mechanisms are typically decided by human experts based on their experience. Only recently the use of automatic FE techniques has been envisaged to cover this [21]. This classic model highly changes with the use of context information and, specifically, localization data. UE based traces imply the use of positioned data that are related to the performance and events happening to specific terminals in the network, where such data are very useful to the analysis of the network issues and behavior. It is typically in a format that does not allow either a clear automation of its process or its integration into the general network management systems. To solve this, and, following the concept of contextualized indicators presented in [20], positioning and network measurements data can be aggregated into time-series metrics that are richer than the purely network-based ones. This is done by means of making weighted statistics of the network measurements, where the weight of each sample is associated to the context (e.g., the position) from where it was gathered. In this way, any probabilistic statistic (mean, median, percentile) can be generated, giving a higher or lower statistical weight (relevance) to each of the measurements. New contextualized metrics can be generated based on different geographic areas, e.g., the mean RSRP experimented in the center of one cell, or in any area of the scenario. Additionally, they can be defined for specific sets of UEs based on their model, applications, services, etc.