Abstract: Systems and methods that use historical data comprising capacity gain solutions and their associated gains at various locations to train a machine learning model. The trained machine learning model, upon receiving a new location (e.g., latitude and longitude coordinates), recommends the top n (e.g., the top 3) solutions that should be deployed at the new location to improve telecommunications network performance. The machine learning model uses clustering techniques to perform the recommendations.

Abstract: Systems and methods that use historical data comprising capacity gain solutions and their associated gains at various locations to train a machine learning model. The trained machine learning model, upon receiving a new location (e.g., latitude and longitude coordinates), recommends the top n (e.g., the top 3) solutions that should be deployed at the new location to improve telecommunications network performance. The machine learning model uses clustering techniques to perform the recommendations.

Abstract: Systems and methods for identifying dominant user locations so that optimum user experience improvement solutions can be deployed at the identified locations are disclosed. One of the purposes of the dominant customer location identification system is to plan for site capacity (for example, small cell planning, hot-spots planning, and dense area capacity planning) and to offer optimum/premium customer experience. The system does this by understanding the customer's dominant locations over a certain period of time (for example, monthly) so that the customer's overall experience can be enhanced. Once a customer's dominant locations are identified, then the telecommunications service provider can gain a better understanding of the primary sites providing service to the customer, and deploy/implement/execute one or more optimum customer experience improvement solutions at the identified sites.

Abstract: Systems and methods are described for monitoring performance and allocating resources to improve the performance of a wireless telecommunications network. A wireless telecommunications network may be comprised of base stations and other infrastructure equipment, which may be sourced from various suppliers. Users may generate traffic on the wireless network, and performance metrics relating to the user experience may be collected from individual base stations. The set of available metrics for a particular base station may vary according to the supplier. A machine learning model is thus trained using metrics from multiple base stations, and used to estimate values for metrics that a particular base station does not provide. The metrics are then further characterized according to the service classes of the users, and resources for improving the performance of base stations are allocated according to the reported and estimated metrics for various service classes.

Abstract: Systems and methods are described for monitoring performance and allocating resources to improve the performance of a wireless telecommunications network. A wireless telecommunications network may be comprised of base stations and other infrastructure equipment, which may be sourced from various suppliers. Users may generate traffic on the wireless network, and performance metrics relating to the user experience may be collected from individual base stations. The set of available metrics for a particular base station may vary according to the supplier. A machine learning model is thus trained using metrics from multiple base stations, and used to estimate values for metrics that a particular base station does not provide. The metrics are then further characterized according to the service classes of the users, and resources for improving the performance of base stations are allocated according to the reported and estimated metrics for various service classes.