### Introducing Composite Indicators  
In this paper, we explore the use of composite indicators as a tool for cognitive management of mobile communications networks, with the aim of evaluating the performance of a network in terms of fairness among clusters of cells, as a first step towards achieving more sustainable infrastructures in the smart city framework. The objective of the paper is to motivate and apply the methodology for the construction of composite indicators in an analysis of a mobile communications network under selected scenarios. Network KPIs are the set of variables that are used in the paper as data to build the composite indicators. In our case, KPIs will be selected following the proposed methodology, in order to cover the main features of the network. Composite indicators have been used as a ranking metric in areas, such as human development [12], global competitiveness [13], or ocean health [14]. Basically, these indicators are the result of a mathematical combination of a set of variables and they help in simplifying complex realities in order to assist decision making processes. On the one hand, composite indicators can successfully synthesize and integrate large amounts of data, and hence, advanced data science software tools are already available to apply this methodology. This particular feature positions composite indicators and the underlying methodology as an interesting tool in future beyond 5G networks, since they may be able to incorporate in a single index the different metrics that were proposed as triggers for autonomous decisions. Mobile network operators (MNO) can then properly weight each one of the metrics following the proposed methodology according to their optimization schemes and business interests. In this case, the composite indicator could serve as a first step, indicating whether a more advanced solution needs to be met. Note that this process could help reducing complexity with an increasing size of the topology, which is one of the drawbacks that calculation of this type of metrics faces. As shown in [15], there are already architectures which allow for data normalization or aggregation towards achieving higher level metrics, which are fundamental steps in the proposed methodology. On the other hand, as described in [16], there is a need to find a flexible methodology that can deal with the fast rate of service changes and the constant technology evolution, as well as to define new user-centric KPIs that could help in linking network KPIs with the end user Quality of Experience (QoE). The Hotspot score is a current solution to rank sectors within a network, but it is really dependent on equipment vendors, operators, and the expertise built over the years. Our aim in this paper is to propose the composite indicator's methodology as a valid framework for dealing with this challenge. Traditional network-centric KPIs have been used in advanced mathematical optimization problems, such as in [17], for example, where the aim is to obtain a multi-criteria based on KPIs to recover the coverage map of a cellular network. However, industry and academy have both been discussing the need for shifting from network-centric KPIs to user-centric KPIs [18], as a bridge that could help in linking KPIs with the end user Quality of Experience (QoE) [16; 19; 20; 21; 22; 23]. Reference [16] explores how to obtain an index outperforming the Hotspot score, by introducing data driven tools that could automate the current empirically-based process. In [19; 20], data are gathered from the users in order to establish the correlation between the users' perception and the actual performance of the network. Note that references [16; 19; 20] do actually have access to massive MNO data. In [21], radio KPIs are combined with user end-to-end performance metrics by introducing the mean opinion score (MOS). The aim is to provide application developers with useful information in order to link network KPIs and user experience.