# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 1 Introduction
[18] also combine both geolocated information coming from third-party applications (e.g., Twitter) with social data for small cell planning purposes. Beyond these approaches, and in order to combine both context (specially localization) and network data in a compatible way with general OAM approaches, the work in [19] defined the concept of _contextualized indicators_. Such indicators are generated by statistically weighting the measurements coming from different UEs, depending on their position in the Area of Interests (AoIs) where they are located. These have the advantage of being easy to integrate into general metrics-based inference mechanisms (e.g., for detection and diagnosis). Although this and the posterior work in [20] have shown the capabilities of such indicators for the detection and diagnosis of cellular failures, they did not address some of the main challenges introduced by the contextualized indicators and that are common to most other approaches for context and location awareness. Firstly, the use of context and, particularly, location, creates new metrics/features (e.g., contextualized indicators) that are unknown by current cellular engineers and staff, which makes them extremely difficult to be \"manually\" defined and for their calculation and properly chosen for their application as inputs of inference rules. Secondly, context variables extremely increase the number of possible indicators that can be used for inference processes. In order to overcome these challenges, the present work develops a novel framework that goes beyond the existing literature via the definition of a novel integrated approach for combining location and network data, with a special focus on ultra-dense indoor scenarios. In this way, a new method is proposed for the automatic generation of different areas of interest and the calculations of its associated metrics. To manage the possibly vast number of generated indicators, feature engineering (FE) techniques are integrated in the system. From this and when considering the problems that FE and inference mechanisms can have when applied to changing environments and systems, a complete approach for their selection and continuous re-training is also defined. In this way, where previous works ignored or just summarily outlined the above-mentioned challenges of context-awareness, the present article thoroughly defines a complete framework to address them, including the automatic integration of location and network data, the necessary FE steps to cope with the resulting increase in dimensionality, and the re-training and selection of the algorithms to maintain their performance. Thus, the paper is organized, as follows. Section 2 analyzes the impact of context-awareness in the management of cellular networks. Section 3 presents the proposed system for its application in cellular network failure management. Section 4 assesses the performance of the framework when applied to cellular failure diagnosis in system level simulations of ultra-dense networks. Finally, Section 5 presents the conclusions and outlook of this work.