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 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 computing a standardized composite gain metric value for each solution that has been previously deployed to fix degradation issues at cell sites or other wireless nodes is disclosed. The method selects a set of Key Performance Indicators (KPIs), each of which is highly correlated to customer experience. For example, the method selects the following KPIs: traffic, numbers of users, Physical Resource Block (PRB) utilization, Channel Quality Indicator (CQI), throughput, and so on. The method then assigns a weight to each KPI, such that the weight reflects each KPI's relative importance and ensures that the KPIs are not double counted. For each solution deployed at a cell site, the method computes values of the following composite gain metrics: weighted gain and offload index. The method then can rank the solutions based on the computed composite gain metric values so that an optimum solution can be selected.

Abstract: Systems and methods for computing a standardized composite gain metric value (e.g., weighted gain or offload index) for each solution that has been previously deployed to fix degradation issues at cell sites or other wireless nodes is disclosed. The method selects a set of Key Performance Indicators (KPIs), each of which is highly correlated to customer experience. For example, the method selects the following KPIs: traffic, number of users, Physical Resource Block (PRB) utilization, Channel Quality Indicator (CQI), throughput, and so on. The method then assigns a weight to each KPI, such that the weight reflects each KPI's relative importance and ensures that the KPIs are not double counted. For each solution deployed at a cell site, the method computes values of the following composite gain metrics: weighted gain and offload index. The method then can rank the solutions based on the computed composite gain metric values so that an optimum solution can be selected.

Abstract: Systems and methods for computing a standardized composite gain metric value (e.g., weighted gain or offload index) for each solution that has been previously deployed to fix degradation issues at cell sites or other wireless nodes is disclosed. The method selects a set of Key Performance Indicators (KPIs), each of which is highly correlated to customer experience. For example, the method selects the following KPIs: traffic, number of users, Physical Resource Block (PRB) utilization, Channel Quality Indicator (CQI), throughput, and so on. The method then assigns a weight to each KPI, such that the weight reflects each KPI's relative importance and ensures that the KPIs are not double counted. For each solution deployed at a cell site, the method computes values of the following composite gain metrics: weighted gain and offload index. The method then can rank the solutions based on the computed composite gain metric values so that an optimum solution can be selected.