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 to identify whether user traffic is generated indoors (e.g., from within a building) or outdoors for a variety of applications, including improving capacity planning, identifying new products offerings, troubleshooting/planning, competitive analysis, planning optimum locations of capacity planning solution deployment, traffic offload analysis, etc. are disclosed. The method receives and aggregates data from a variety of sources, including customer geolocation data, network data, street/building maps, indoor/outdoor classification of traffic, etc. to generate demand density maps that depict network traffic usage patterns at a building level. The method can then use the demand density maps to identify hotspots, evaluate in-building coverage, and select and rank optimum solutions and/or locations for capacity improvement solutions deployment.

Abstract: Systems and methods to identify whether user traffic is generated indoors (e.g., from within a building) or outdoors for a variety of applications, including improving capacity planning, identifying new products offerings, troubleshooting/planning, competitive analysis, planning optimum locations of capacity planning solution deployment, traffic offload analysis, etc. are disclosed. The method receives and aggregates data from a variety of sources, including customer geolocation data, network data, street/building maps, indoor/outdoor classification of traffic, etc. to generate demand density maps that depict network traffic usage patterns at a building level. The method can then use the demand density maps to identify hotspots, evaluate in-building coverage, and select and rank optimum solutions and/or locations for capacity improvement solutions deployment.

Abstract: Systems and methods to identify whether user traffic is generated indoors (e.g., from within a building) or outdoors for a variety of applications, including improving capacity planning, identifying new products offerings, troubleshooting/planning, competitive analysis, planning optimum locations of capacity planning solution deployment, traffic offload analysis, etc. are disclosed. The method receives and aggregates data from a variety of sources, including customer geolocation data, network data, street/building maps, indoor/outdoor classification of traffic, etc. to generate demand density maps that depict network traffic usage patterns at a building level. The method can then use the demand density maps to identify hotspots, evaluate in-building coverage, and select and rank optimum solutions and/or locations for capacity improvement solutions deployment.

Abstract: Systems and methods to understand the roadways (roads, highways, etc.) where customers are experiencing network congestion and the time of day when customer experience is degraded are disclosed. The method receives and aggregates data from a variety of sources, including customer data (e.g., speed, experience throughputs, reported coverage, etc.), network data (site coverage—RSRP/RSRQ, site capacity—users and bandwidth), maps/traffic data (speed limit), etc. to measure both vehicular congestion and network congestion on roadways. Ultimately, the method can generate an enhanced map that merges vehicular traffic congestion and network traffic congestion to present feature-added routes to a user. A user can then use the enhanced map to better plan their trip to ensure maximum network coverage during their trip.

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 to identify whether user traffic is generated indoors (e.g., from within a building) or outdoors for a variety of applications, including improving capacity planning, identifying new products offerings, troubleshooting/planning, competitive analysis, planning optimum locations of capacity planning solution deployment, traffic offload analysis, etc. are disclosed. The method receives and aggregates data from a variety of sources, including customer geolocation data, network data, street/building maps, indoor/outdoor classification of traffic, etc. to generate demand density maps that depict network traffic usage patterns at a building level. The method can then use the demand density maps to identify hotspots, evaluate in-building coverage, and select and rank optimum solutions and/or locations for capacity improvement solutions deployment.

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 to identify whether user traffic is generated indoors (e.g., from within a building) or outdoors for a variety of applications, including improving capacity planning, identifying new products offerings, troubleshooting/planning, competitive analysis, planning optimum locations of capacity planning solution deployment, traffic offload analysis, etc. are disclosed. The method receives and aggregates data from a variety of sources, including customer geolocation data, network data, street/building maps, indoor/outdoor classification of traffic, etc. to generate demand density maps that depict network traffic usage patterns at a building level. The method can then use the demand density maps to identify hotspots, evaluate in-building coverage, and select and rank optimum solutions and/or locations for capacity improvement solutions deployment.

Abstract: Systems and methods to understand the roadways (roads, highways, etc.) where customers are experiencing network congestion and the time of day when customer experience is degraded are disclosed. The method receives and aggregates data from a variety of sources, including customer data (e.g., speed, experience throughputs, reported coverage, etc.), network data (site coverage-RSRP/RSRQ, site capacity-users and bandwidth), maps/traffic data (speed limit), etc. to measure both vehicular congestion and network congestion on roadways. Ultimately, the method can generate an enhanced map that merges vehicular traffic congestion and network traffic congestion to present feature-added routes to a user. A user can then use the enhanced map to better plan their trip to ensure maximum network coverage during their trip.

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 to detect abnormal behavior of cell sites and/or customers are disclosed. By detecting cell site and/or customer behavior anomalies, the system enhances capacity planning by helping understand congestion, more efficiently planning event sites, suggesting installation of temporary solutions, identifying when true traffic needs are increased, and detecting abnormal customer behavior and/or demand. The system accesses historical data for a set of KPIs (e.g., 3 months of hourly data that captures traffic, users, Physical Resource Block (PRB), and throughput). The system computes, for periodic time intervals (e.g., for each hour and each day), upper and lower limits for each site and for each KPI. Using this information, the system detects anomalies for current KPI measurements. The system sends alerts when anomalies are detected for a threshold period of time (e.g., when the KPI measurement falls outside of the computed upper and/or lower bounds continuously for 2-3 hours).

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 to understand the roadways (roads, highways, etc.) where customers are experiencing network congestion and the time of day when customer experience is degraded are disclosed. The method receives and aggregates data from a variety of sources, including customer data (e.g., speed, experience throughputs, reported coverage, etc.), network data (site coverage—RSRP/RSRQ, site capacity—users and bandwidth), maps/traffic data (speed limit), etc. to measure both vehicular congestion and network congestion on roadways. Ultimately, the method can generate an enhanced map that merges vehicular traffic congestion and network traffic congestion to present feature-added routes to a user. A user can then use the enhanced map to better plan their trip to ensure maximum network coverage during their trip.

Abstract: Systems and methods for overall customer experience and scoring system to compute an overall score for each customer of a telecommunications service provider based on one or more customer-experience factors are disclosed. The customer scoring system selects a subset of customer-experience factors and for certain sectors, it computes a score value and a weight value for each customer-experience factor. Using these computed values, the system computes, for the customer, a customer network user experience score for the sector. The system then computes an overall customer network user experience score for the customer using the computed customer network user experience scores and a weight value of each sector. Computing and assigning an overall score to each customer helps the system to understand and differentiate between customer experiences in various markets, and identify customer experience enhancement actions to perform in various market.

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 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 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.

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.