Abstract:
A method includes obtaining device data by a network, wherein the network collects data from a plurality of connected devices, selecting a key performance indicator associated with the plurality of connected devices, clustering the data in accordance with the key performance indicator to form a plurality of clustered data sets, and determining a pattern within at least one of the plurality of clustered data sets to recommend network resource allocations. The pattern may be further used to analyze a second set of device data to determine an updated pattern and wherein the updated pattern is determined based on the pattern and the second set of device data.
Abstract:
The system allows real time collection and processing of massive data from many network elements. It has an elastic architecture that scales horizontally to support different network sizes. The system in a uniform data format for downstream consumption. It employs a pub/sub data distribution mechanism that supports multiple concurrent downstream subscribers efficiently in real-time.
Abstract:
A method includes detecting, at a customer premise equipment management system, a trigger event associated with customer premise equipment data, the customer premise equipment data associated with a customer premise equipment device. The method also includes initiating a connection to the customer premise equipment device via an application program interface in response to the trigger event, where the application program interface is selected based on the customer premise equipment data. The method further includes sending, from the customer premise equipment management system, a customer premise equipment data request to the customer premise equipment device via the application program interface.
Abstract:
Aspects of the subject disclosure may include, for example, a method performed by a processing system; the method includes receiving a plurality of values of key performance indicators (KPIs) relating to performance of a cell on a communication network. The plurality of values of the KPIs includes labeled training data for training a machine learning (ML) model for the performance of the cell. The method further includes iteratively executing, using the labeled training data, a training procedure for the ML model; and testing the trained ML model. The labeled training data corresponds to ground truth data that may include a training data set, a validation data set and a test data set. The trained ML model, when deployed on a communication network, receives as input near-real time data regarding the performance of the cell and provides as output predictions of the performance of the cell. Other embodiments are disclosed.
Abstract:
The system allows real time collection and processing of massive data from many network elements. It has an elastic architecture that scales horizontally to support different network sizes. The system in a uniform data format for downstream consumption. It employs a pub/sub data distribution mechanism that supports multiple concurrent downstream subscribers efficiently in real-time.
Abstract:
Concepts and technologies are disclosed herein for using user equipment data clusters and spatial temporal graphs of abnormalities for root cause analysis. User equipment data can be obtained from a cellular network. A filter having a threshold can be applied to the user equipment data to obtain records. A determination is made whether the threshold is to be adaptively adjusted. If a determination is made that the threshold is not to be adjusted, the records can be added to a record set. The records in the subset of records can be correlated based on a key to obtain a filtered and correlated version of the record set, a spatial temporal graph of abnormalities associated with the cellular network can be generated based on the filtered and correlated version of the record set, and a root cause of a failure can be determined based on the spatial temporal graph of abnormalities.
Abstract:
Aspects of the subject disclosure may include, for example, determining a group of coefficients for a digital filter, and configuring the digital filter according to the group of coefficients. Further embodiments can include processing a group of statistics associated with a first data flow utilizing the digital filter, and determining a first statistic from the group of statistics does not satisfy a statistical threshold resulting in a first determination. Other embodiments are disclosed.
Abstract:
Spatial-temporal informative patterns for users and devices associated with data networks can be predicted or determined. An information management component (IMC) can analyze respective groups of items of data stored in respective formats in respective databases. Some items of data can comprise respective signal measurement data representative of respective signal measurements associated with respective devices associated with a communication network. Based on the analysis results, IMC can determine a spatial-temporal pattern(s) associated with the respective groups of items of data, wherein the spatial-temporal pattern(s) can relate to a subject of interest. The IMC can utilize artificial intelligence and/or machine learning algorithms and models to facilitate determining the spatial-temporal pattern(s). In response to a query relating to the subject of interest, the IMC can provide information relating to the subject of interest and responsive to the query based on the spatial-temporal pattern(s).
Abstract:
Aspects of the subject disclosure may include, for example, receiving a request from a mobile network entity for a key performance indicator (KPI) prediction over a portion of a mobile network, and obtaining a group of identifiers associated with the mobile network entity. Further embodiments can include obtaining a group of KPIs associated with the mobile network entity based on the group of identifiers, and determining a KPI prediction associated with the mobile network entity based on the group of KPIs. Additional embodiments can include allocating a group of network resources to the mobile network entity based on the KPI prediction. Other embodiments are disclosed.
Abstract:
Aspects of the subject disclosure may include, for example, a method in which a processing system coupled to a communication network identifies, based on base station unit (BSU) performance measurements and network key performance indicator (KPI) measurements, a set of BSUs experiencing interference. The method further includes selecting BSUs from the identified set that are to be provided with an auxiliary antenna array; and computing, for each BSU in the identified set, parameters to be used by modules of the BSU that are local to the BSU and configured to perform interference detection, interference estimation, and/or interference cancellation; the computed parameters are used in a reconfiguration procedure for the modules of the BSU. Other embodiments are disclosed.