Abstract: Systems and methods for managing telecommunication traffic for a wireless communication network associated with multiple coverage areas (“cells”) are disclosed. The system may detect congested cells of the wireless communication network, identify suitable neighboring cells for offloading traffic from the congested cells, and implementing handovers from congested cells to suitable neighbor cells to balance the wireless communication network traffic.

Abstract: The system obtains KPIs of a cell of a telecommunication network. The system preprocesses the multiple KPIs by reducing noise of the multiple KPIs and normalizing an amplitude of a KPI among the multiple KPIs. The system identifies a performance issue of the network by executing multiple ML models based on the KPIs, where a first ML model is configured to identify a sudden performance degradation, where a second ML model is configured to identify an event leading to a change in a performance trend, where a third ML model is configured to identify a gradual change in the performance trend, and where a fourth ML model is configured to identify whether the cell of the network is nearing a capacity limitation of the cell. The system provides a report based on the identified performance issue.

Abstract: A computer system obtains information describing a geographical area segmented into multiple first clusters serviced by a telecommunications network. Multiple test locations are identified within the first clusters. Each test location is located within a grid of the geographical area. Each first cluster is recursively segmented into multiple second clusters until a difference between a number of test locations within each second cluster and a target number of test locations is less than a threshold number of test locations. A route is generated connecting test locations within each second cluster, using a routing application programming interface for performing drive testing of the telecommunications network. The computer system sends the route to one or more computer devices for performing the drive testing at the test locations in a sequence corresponding to the route.

Abstract: The described technology provides a system and method for identifying cells in a wireless communication network for corrective action to improve coverage and capacity of the network. A coverage analysis tool receives network traffic information, extracts call or data traffic information for a particular radio access technology (RAT), and determines based on radio frequency (RF) information and distances between serving cells and call data, and serving cells and adjacent neighbor cells, whether the serving cells are over-shooter or under-shooter cells. The coverage analysis tool recommends remedial action to improve the network's coverage and capacity including adjusting antenna tilt and/or adjusting transmit power.

Abstract: The described technology provides a system and method for determining, inter alia, if a serving cell in a cellular network is an over-shooter cell serving calls outside an intended or designed coverage area by searching for eligible neighbor cells in a search area bounded by an azimuth of a sector antenna of the serving cell. The serving cell is determined to be an over-shooter cell when, e.g., the distance between the serving cell and mobile devices associated with the calls is larger than the average distance between the serving cell and one or more closest neighbor cells in a list of eligible neighbor cells identified in the search area.

Abstract: Machine-learning based techniques are described herein for determining and modifying handover parameters within multilayer wireless networks. Various communication session data, such as key performance indicators, may be analyzed and compared at multiple frequency layers to determine sets of custom parameters associated with one or more wireless networks. The sets of custom parameters and network performance data may be used to train one or more machine-learned models to improve and/or optimize the handover parameters used by the network nodes. In some examples, different trained models may be associated with different network performance metrics, such as throughput optimization, network speed, and/or dropped call minimization, etc. A trained machine-learned model may be used to analyze the session data from a set of network nodes, and to determine or tune the handover parameters used by the network nodes.

Abstract: A cell coverage management service associated with a wireless communication service provider may collect geolocated traffic data related to coverage areas of the provider's wireless communication network. The service may generate and update associations between geolocations and cells based on the traffic data and determine defects or deficiencies in the current coverage among cells. The service may further identify particular remedies or solutions to address the defects or deficiencies and implement the remedies at corresponding cell sites.

Abstract: A cell coverage management service associated with a wireless communication service provider may collect geolocated traffic data related to coverage areas of the provider's wireless communication network. The service may generate and update associations between geolocations and cells based on the traffic data and determine defects or deficiencies in the current coverage among cells. The service may further identify particular remedies or solutions to address the defects or deficiencies and implement the remedies at corresponding cell sites.

Abstract: Systems and methods for managing telecommunication traffic for a wireless communication network associated with multiple coverage areas (“cells”) are disclosed. The system may detect congested cells of the wireless communication network, identify suitable neighboring cells for offloading traffic from the congested cells, and implementing handovers from congested cells to suitable neighbor cells to balance the wireless communication network traffic.

Abstract: The techniques and systems described herein are directed, in part, to optimizing network performance by clustering cell sites of the network based on performance patterns and then optimizing each cluster of cell sites, thereby optimizing performance of a network as a whole. The cell sites may be base stations, radio access networks, and/or other hardware that directly or indirectly exchanges communications with user devices such as mobile telecommunication devices (e.g., user handsets, user hardware, etc.). By optimizing the clusters of cell sites, a service provider (e.g., a telecommunications company, etc.) may improve network performance in less time and/or with less capital resources than attempting to optimize each cell site on an individual basis.