# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 1 Introduction  
Until recently, mechanisms that are applied to implement performance analysis and failure management activities in cellular networks have been typically based on the analysis of alarms, radio measurements, and network performance indicators (e.g., throughput) [1]. However, this approach has become very limited due to the complexity of the new mobile scenarios and, in particular, for ultra-dense cellular environments. The monitoring of these deployments, increasingly common and one of the expected key scenarios in 5G, implies important challenges [2] due to the high dynamic nature of their user distributions, their fast-changing performance, coverage overlapping, and very variable traffic demand. Therefore, the effectiveness of mechanisms that are purely based on network performance, like the ones followed by previous approaches, is highly diminished. Conversely, the improvement of User Equipment (UE) positioning in both outdoors and indoors [3, 4, 5, 6], allows for the generation and application of position information for operations, administration and maintenance (OAM) automatic mechanisms [7, 8]. Hence, different works and tools have been developed for location-aware failure management mechanisms. However, these approaches are only typically employed for very specific applications that are not integrated into a general failure management architecture and in macrocell scenarios. These systems typically rely on geolocated UE traces obtained via drive tests, Global Navigation Satellite System (GNSS)-based third-party apps measurements, as well as UE traces provided by the network itself (and typically positioned bycellular signals localization techniques), typically known as Minimization of Drive Tests (MDT) or UE network-based traces [9]. As it is going to be further described in Section 2, these geolocated traces had a very different format to the classical network management variables, such as counters and alarms, which are processed by well-known algorithms for events and time-series. This makes the use of this location information to commonly rely on direct human inspection of the generated positioned map-like information. However, some works have approached the automated application of UE positioned data for cellular management. A particular application of these traces is defined in [10], where UE geolocated data are used for coverage estimation. The work in [11] proposes a specific framework for the detection and compensation of cell outages based on positioned traces and their processing using Support vector data description (SVDD). Alternatively, geographical related information, such as the distance between UEs and the base station (BS), have been considered as possible variables to be used by failure management mechanisms. Particularly, [PERSON] et al. proposes an unsupervised clustering of network failures while considering such distance as an input for the algorithms [12]. Other works have focused on the application of MDT for the detection of specific problems, such as \"sleeping cells\", by means of different techniques, such as diffusion maps [13] or N-gram analysis [14]. Other works revolve around the wider _context-awareness_ concept [2]. This paradigm is defined by the inclusion of non-network _context_ variables into the cellular management procedures. This is particularly relevant, as many variables that are outside of the network itself have a huge impact in the communications performance. Indeed, examples of these variables include the UEs' positions, but also the applications being executed, battery or positioning of the users, as well as social events [15; 16]. In this line, the work in [17] presents an automatic system for cellular network diagnosis using data from large-scale monitoring. Such a method can use many categorical features (devices, services, and user groups) and identify its relevance in the diagnosis. Nevertheless, the work does not consider the UEs' positions. [PERSON] et al.