At&t Intellectual Property I, LP

Other

Mehdi Malboubi

Baofeng Jiang

Abhijeet Bhorkar

Raghvendra Savoor

Hyun Ok Lee

Yuhong Zheng

Eric Jung

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.

Using user equipment data clusters and spatial temporal graphs of abnormalities for root cause analysis

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.

Methods, systems, and devices for scalable and layered architecture for real-time key performance indicator (KPI) prediction in mobile networks

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.

Automatic and real-time cell performance examination and prediction in communication networks

The disclosed technology is directed towards partitioning data and distributing the data to different storage locations, which facilitates better data security. For example, a large database of source data can be partitioned into a small enabler partition and one or more large partitions, in which a full set of the partitions is needed to reconstruct the source data to its original state. The large partition can be maintained at an edge computing facility to reduce latency, or at a cloud computing facility to reduce storage expenses, with the smaller enabler partition only accessed when needed to reconstruct the data. A database is partitioned into a group of partitions, and the group of partitions is distributed to separate storage facilities. The separate storage and computing facilities/nodes are accessed to obtain datasets of the group of partitions, and merged to reconstruct the source data.

Storing data at edges or cloud storage with high security

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.

Methods, systems, and devices for software defined network monitoring

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).

Determining spatial-temporal informative patterns for users and devices in data networks

Resources associated with a responder communication network and a communication network can be managed in an effective manner. In connection with an event, a resource management component (RMC) can analyze network-related data associated with the networks and external data relating to the event or a geographic area related thereto. In connection with the event, based on the analysis, RMC can desirably manage the resources, in part, by determining locations or adjustments for portable base stations, sensors, and/or devices associated with the responder communication network to facilitate high quality communication of information, determining traffic routes and other path planning for vehicles or personnel, creating network slices for high quality communication of information, and/or performing monitoring and intelligent troubleshooting with regard to the networks. RMC can employ artificial intelligence or machine learning techniques and models to facilitate making desired predictions or inferences relating to the event or networks.

Intelligent support framework usable for enhancing responder network

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.

Network centric interference and noise cancellation in wireless networks

Aspects of the subject disclosure may include, for example, a method in which a processing system identifies a set of target users of user equipment communication devices (UEs), based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs; obtaining from internal sources a set of key performance indicators (KPIs) for the communication network; correlating information received from external sources with the data obtained from the internal sources to validate the reports from the UEs; and recommending, in accordance with data records generated by the correlating, an action to improve the QoS for a UE of the set of UEs, where the action includes a modification of the UE and/or a reconfiguration of the network. Other embodiments are disclosed.

Intelligent customer oriented mobility network engineering at edges

Resources associated with a responder communication network and a communication network can be managed in an effective manner In connection with an event, a resource management component (RMC) can analyze network-related data associated with the networks and external data relating to the event or a geographic area related thereto. In connection with the event, based on the analysis, RMC can desirably manage the resources, in part, by determining locations or adjustments for portable base stations, sensors, and/or devices associated with the responder communication network to facilitate high quality communication of information, determining traffic routes and other path planning for vehicles or personnel, creating network slices for high quality communication of information, and/or performing monitoring and intelligent troubleshooting with regard to the networks. RMC can employ artificial intelligence or machine learning techniques and models to facilitate making desired predictions or inferences relating to the event or networks.

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 a 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.

Concepts and technologies are disclosed for creating and using cell clusters. Cellular network data associated with a cellular network can be obtained. The cellular network data can include configuration data associated with a cell of the cellular network and a performance indicator associated with the cellular network. A number of cell clusters to be generated can be determined and the cell clusters can be generated. The cell clusters can include a cell cluster that can represent multiple cells including the cell. A model that represents the cell cluster can be trained. An input cluster that represents multiple inputs can be generated. The inputs can be associated with the multiple cells and the input cluster can include a value. The value can be provided as input to the model to obtain a predicted output associated with the cell cluster.

Creating and using cell clusters

Aspects of the subject disclosure may include, for example, a method in which a processing system receives data at an edge node of a network that also includes regional nodes and central nodes. The processing system also determines a latency criterion associated with an application for processing the data; the application corresponds to an application programming interface. The method also includes processing the data in accordance with the application, monitoring a latency associated with the processing, and determining whether the latency meets the latency criterion. The processing system dynamically assigns data processing resources so that the latency meets the latency criterion; the resources include computation, network and storage resources of the edge node, a central node, and a regional node in communication with the edge node and the central node. Other embodiments are disclosed.

System and method for low latency edge computing

A framework to handle monitoring and automatic fault manifestation in cloud networks. Multiple techniques correlate the logs of different cloud services or generate independent capsules for each component, VM, storage, or transaction. In a first exemplary technique, an authentication token is provided by an authentication service for logs during a period of an event. In a second exemplary technique, a unique instance ID for multiple distinct processes may be created in a data model of notification logs or service logs.

Growing community of inventors

San Ramon, CA, United States of America

Mehdi Malboubi