Applied sciences  
Article  
Mobile Networks  
[PERSON] *, [PERSON] and [PERSON]  
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the localized mobile user characteristics of each different zone. This specialization process provides the best possible coverage with the lowest interference, resulting in high quality of service. Radio planning teams traditionally use an iterative method of re-configuring cell parameters to maximize the performance of these cell layers. During this process, network measurements are being collected that are then used to assist the parameter benchmarking. These measurements are either performed by specialized hardware operated by radio engineers, or they can be based on probes and real mobile terminals that operate in test modes. The generated datasets consist of network records that contain signaling information related to network operations and actions such as channel quality measurements and network-related information from upper network layers. The inclusion of location information in these measurements is of key importance for the identification of coverage deficiencies and optimum network reconfiguration [4; 5; 6; 7; 8]. To acquire the location information, various techniques [9; 10; 11] have been shown to estimate the mobile terminal's location within a specific zone by using only the radio channel quality measurements performed by the mobile terminal devices during handover-related operations. Areas such as university campuses, city malls, and entertainment venues (that are generally placed right outside the urban zones) have very complex population density and traffic demand patterns related to other more rural zones. These factors are causing the static planning techniques to underperform and the need has risen for more advanced analytical solutions that can leverage mobile terminal location information. This information has great benefits as it improves the accuracy of the detection of quality degradation metrics to a specific point in the map (e.g., a traffic hot-zone). Analysis of the measurement data can also be conducted with automated methods [12] to generate reconfiguration actions that will change the layout of the network, further improving the radio coverage and quality and increasing the capacity layer's performance. In this paper, we analyzed the literature of model-based and data-driven algorithms for cell reconfiguration, and focused on unsupervised learning [13; 14; 15; 16] methodologies that have proven their effectiveness in a large number of network-related optimization tasks. Traditional approaches such as the clustering of network KPIs has been shown to correctly detect and segment the network elements into meaningful groups, which greatly reduces the processing overhead and is very robust to the noisy nature of network data. The inclusion of location information in measurement datasets makes algorithms that perform spatial clustering a key asset to the discovery of underlying human and traffic hot-zones. In this research, we are proposing a novel algorithmic scheme that combines the merits of both the network KPI and the location data clustering techniques.