Abstract: Systems and methods are described for providing a virtual machine (“VM”) as a service. A user device can install a VM to enable itself as an edge node. The user device can then and use a portion of its computing resources to provide the service to the endpoint device by running the VM. In an example, an edge node can directly receive a request for a service from an endpoint device. The edge node can determine that it needs assistance from another device to jointly provide the service. Then another user device which is available to operate as an edge node can join the edge team.

Abstract: Systems and methods are described for providing a virtual machine (“VM”) as a service. A user device can install a VM to enable itself as an edge node. The user device can then and use a portion of its computing resources to provide the service to the endpoint device by running the VM. In an example, an edge node can directly receive a request for a service from an endpoint device. The edge node can determine that it needs assistance from another device to jointly provide the service. Then another user device which is available to operate as an edge node can join the edge team.

Abstract: A system can include a network analysis platform for a two-level grid-based anomaly area identification and solution nomination in a radio access network. The network analysis platform can map key performance indicators for user sessions in the network to a grid that overlays a geographic area. The grid can be based on a military grid reference system. A machine-learning model can take a vector of key performance indicator samples as input and identify a problem for the grid. The network analysis platform can nominate cells to attempt to remediate based on ranking poor performing bins in the grid and determining the cells that contribute most to the problem in each bin. For a nominated cell, the network analysis platform can perform remediation actions to solve a coverage problem, throughput problem, or both. The problem can be solved for the grid while preventing conflicts between individual cells.

Abstract: A system can include a network analysis platform for a two-level grid-based anomaly area identification and solution nomination in a radio access network. The network analysis platform can map key performance indicators for user sessions in the network to a grid that overlays a geographic area. The grid can be based on a military grid reference system. A machine-learning model can take a vector of key performance indicator samples as input and identify a problem for the grid. The network analysis platform can nominate cells to attempt to remediate based on ranking poor performing bins in the grid and determining the cells that contribute most to the problem in each bin. For a nominated cell, the network analysis platform can perform remediation actions to solve a coverage problem, throughput problem, or both. The problem can be solved for the grid while preventing conflicts between individual cells.

Abstract: A network analysis and management system includes a processor coupled to a communications interface and a non-transitory memory. The processor is configured to read instructions from the non-transitory memory to cause the system to perform operations comprising receiving log data associated with communication between a first user device and a network using a first protocol stack including a plurality of layers. A plurality of network events are generated based on the log data. Each network event is associated with a respective log data entry of the log data and a respective layer of the plurality of layers. First network analysis information is generated by correlating first and second network events using a correlation configuration associated with a network management task. A network management message is generated using the first network analysis information, and transmitted to one of the first user device and the network for performing the network management task.

Abstract: A processing system including at least one processor may obtain a request that identifies a region associated with a wireless communication network, identify sub-areas within the region, obtain performance indicator data for the sub-areas for a plurality of time periods from mobile devices within each sub-area, and apply an adaptive thresholding to the performance indicator data for each sub-area to determine one or more thresholds associated with the performance indicator data. The processing system may further identify a risk level, from among a plurality of risk levels, for each sub-area for at least one time periods based upon the one or more thresholds and the performance indicator data for each sub-area, where the one or more thresholds define one or more risk levels of the plurality of risk levels, and provide reporting data indicating the risk level for each sub-area for the at least one time period.

Abstract: Small cell identification using machine learning is provided. A method can include extracting, by a device comprising a processor, signal strength information for a cell in a cellular communication network from user equipment log data; estimating, by the device, path loss information associated with the cell at respective distances based on the signal strength information for the cell, resulting in estimated path loss information; and, based on the estimated path loss information, optionally along with other information such as antenna transmission power, antenna transmission frequency band, percentage of user equipments having a distance to a base station within a threshold, maximum user equipment distance to a base station, etc., classifying, by the device, the cell as a type from a group of types of cells, the group comprising a macro cell and a small cell.

Abstract: A system can include a network analysis platform for a two-level grid-based anomaly area identification and solution nomination in a radio access network. The network analysis platform can map key performance indicators for user sessions in the network to a grid that overlays a geographic area. The grid can be based on a military grid reference system. A machine-learning model can take a vector of key performance indicator samples as input and identify a problem for the grid. The network analysis platform can nominate cells to attempt to remediate based on ranking poor performing bins in the grid and determining the cells that contribute most to the problem in each bin. For a nominated cell, the network analysis platform can perform remediation actions to solve a coverage problem, throughput problem, or both. The problem can be solved for the grid while preventing conflicts between individual cells.

Abstract: A processing system including at least one processor may obtain a request that identifies a region associated with a wireless communication network, identify sub-areas within the region, obtain performance indicator data for the sub-areas for a plurality of time periods from mobile devices within each sub-area, and apply an adaptive thresholding to the performance indicator data for each sub-area to determine one or more thresholds associated with the performance indicator data. The processing system may further identify a risk level, from among a plurality of risk levels, for each sub-area for at least one time periods based upon the one or more thresholds and the performance indicator data for each sub-area, where the one or more thresholds define one or more risk levels of the plurality of risk levels, and provide reporting data indicating the risk level for each sub-area for the at least one time period.

Abstract: A processing system including at least one processor may obtain a request that identifies a region associated with a wireless communication network, identify sub-areas within the region, obtain performance indicator data for the sub-areas for a plurality of time periods from mobile devices within each sub-area, and apply an adaptive thresholding to the performance indicator data for each sub-area to determine one or more thresholds associated with the performance indicator data. The processing system may further identify a risk level, from among a plurality of risk levels, for each sub-area for at least one time periods based upon the one or more thresholds and the performance indicator data for each sub-area, where the one or more thresholds define one or more risk levels of the plurality of risk levels, and provide reporting data indicating the risk level for each sub-area for the at least one time period.

Abstract: By utilizing a database module, retrieving configuration management data related to network parameters can generate additional network efficiencies. The database module can be strategically placed to be utilized by various activities such as system network optimization, load balancing, network coverage, etc. The database module can also be utilized to automatically print out a radio frequency data sheet and utilized to perform network planning, generate updated sector information and import manually data that is currently procured manually.

Abstract: Small cell identification using machine learning is provided. A method can include extracting, by a device comprising a processor, signal strength information for a cell in a cellular communication network from user equipment log data; estimating, by the device, path loss information associated with the cell at respective distances based on the signal strength information for the cell, resulting in estimated path loss information; and, based on the estimated path loss information, optionally along with other information such as antenna transmission power, antenna transmission frequency band, percentage of user equipments having a distance to a base station within a threshold, maximum user equipment distance to a base station, etc., classifying, by the device, the cell as a type from a group of types of cells, the group comprising a macro cell and a small cell.

Abstract: Small cell identification using machine learning is provided. A method can include extracting, by a device comprising a processor, signal strength information for a cell in a cellular communication network from user equipment log data; estimating, by the device, path loss information associated with the cell at respective distances based on the signal strength information for the cell, resulting in estimated path loss information; and, based on the estimated path loss information, optionally along with other information such as antenna transmission power, antenna transmission frequency band, percentage of user equipments having a distance to a base station within a threshold, maximum user equipment distance to a base station, etc., classifying, by the device, the cell as a type from a group of types of cells, the group comprising a macro cell and a small cell.

Abstract: By utilizing a database module, retrieving configuration management data related to network parameters can generate additional network efficiencies. The database module can be strategically placed to be utilized by various activities such as system network optimization, load balancing, network coverage, etc. The database module can also be utilized to automatically print out a radio frequency data sheet and utilized to perform network planning, generate updated sector information and import manually data that is currently procured manually.

Abstract: Small cell identification using machine learning is provided. A method can include extracting, by a device comprising a processor, signal strength information for a cell in a cellular communication network from user equipment log data; estimating, by the device, path loss information associated with the cell at respective distances based on the signal strength information for the cell, resulting in estimated path loss information; and, based on the estimated path loss information, optionally along with other information such as antenna transmission power, antenna transmission frequency band, percentage of user equipments having a distance to a base station within a threshold, maximum user equipment distance to a base station, etc., classifying, by the device, the cell as a type from a group of types of cells, the group comprising a macro cell and a small cell.

Abstract: A network analysis and management system includes a processor coupled to a communications interface and a non-transitory memory. The processor is configured to read instructions from the non-transitory memory to cause the system to perform operations comprising receiving log data associated with communication between a first user device and a network using a first protocol stack including a plurality of layers. A plurality of network events are generated based on the log data. Each network event is associated with a respective log data entry of the log data and a respective layer of the plurality of layers. First network analysis information is generated by correlating first and second network events using a correlation configuration associated with a network management task. A network management message is generated using the first network analysis information, and transmitted to one of the first user device and the network for performing the network management task.

Abstract: A network analysis and management system includes a processor coupled to a communications interface and a non-transitory memory. The processor is configured to read instructions from the non-transitory memory to cause the system to perform operations comprising receiving log data associated with communication between a first user device and a network using a first protocol stack including a plurality of layers. A plurality of network events are generated based on the log data. Each network event is associated with a respective log data entry of the log data and a respective layer of the plurality of layers. First network analysis information is generated by correlating first and second network events using a correlation configuration associated with a network management task. A network management message is generated using the first network analysis information, and transmitted to one of the first user device and the network for performing the network management task.

Abstract: A network analysis and management system includes a processor coupled to a communications interface and a non-transitory memory. The processor is configured to read instructions from the non-transitory memory to cause the system to perform operations comprising receiving log data associated with communication between a first user device and a network using a first protocol stack including a plurality of layers. A plurality of network events are generated based on the log data. Each network event is associated with a respective log data entry of the log data and a respective layer of the plurality of layers. First network analysis information is generated by correlating first and second network events using a correlation configuration associated with a network management task. A network management message is generated using the first network analysis information, and transmitted to one of the first user device and the network for performing the network management task.