At&t Intellectual Property I, L.p.

At&t Mobility Ii LLC

Pontificia Universidade Catolica Do Rio De Janeiro

Carlos Eduardo De Andrade

Weiyi Zhang

Sarat Puthenpura

Rakesh Sinha

Vijay Gopalakrishnan

Ajay Mahimkar

Slawomir Mikolaj Stawiarski

Emir Halepovic

Simon D Byers

Christopher Volinsky

Lien K Tran

David John Poole

Nemmara K Shankaranarayanan

Robert Riding

Will Adams Culpepper

Wenjie Zhao

Wei Yuan

Yuning Yang

Luciana De Souza Pessôa

Mariana Alves Londe

Edward Sherman

Christopher Boaz

Aspects of the subject disclosure may include, for example, determining regression models that predict resource utilization at security gateways as a function of performance indicator values measured at radio access network (RAN) nodes. Optimization models are built using the regression models and constraints related to the security gateway resources. Solutions to the optimization models include assignments of RAN nodes to communicate with security gateways, where the assignments satisfy the constraints in the optimization models. Other embodiments are disclosed.

(RE) assigning of nodes on security and priority network

Aspects of the subject disclosure may include, for example, obtaining a natural language statement of an optimization problem that is to be solved, analyzing the natural language statement to select a template included within a plurality of templates, generating a definition for the optimization problem using the template, constructing a model in accordance with the definition, resulting in a constructed model, and validating the constructed model, resulting in a validated model. Other embodiments are disclosed.

Apparatuses and methods for facilitating solutions to optimization problems via modeling through natural language processing and machine learning

A new scalable approach to conflict-free deployment of changes across networks. The conflict rules or constraints may be modeled using policies and algorithms to determine an optimized schedule for change deployment.

Conflict-free change deployment

Aspects of the subject disclosure may include, for example, obtaining a first plurality of inputs that identify a plurality of geographical locations and a plurality of infrastructure located at the plurality of geographical locations, classifying each of the plurality of geographical locations in accordance with the first plurality of inputs to obtain a plurality of classes, obtaining a second plurality of inputs that identify costs, revenue, profits, or any combination thereof, associated with the plurality of infrastructure, processing the second plurality of inputs in conjunction with the plurality of classes to identify a first plurality of locations included in the plurality of geographical locations to decommission infrastructure included in the plurality of infrastructure, and presenting the first plurality of locations via a device. Other embodiments are disclosed.

Apparatuses and methods for identifying infrastructure through machine learning

A framework referred to as COmposition fRamework for chaNge management (CORNET) may integrate re-usable abstraction, modular composition with plug-and-play capabilities, or automated translation of high-level change management intent into low-level implementations and mathematical models. CORNET may use real-world data collected from cellular networks (e.g., 4G or 5G) and virtualized services, such as virtual private networks (VPN) and software defined wide area networks (SDWAN) running in the cloud.

Change deployment system

Facilitating assignment of root sequence index while minimizing network changes is provided herein. Operations of a system include determining an initial assignment of root sequence indexes for a group of network equipment. The operations can also include evaluating the initial assignment for first assignments that satisfy a defined criterion and second assignments that fail to satisfy the defined criterion. Based on the defined criterion, the operations can include updating the second assignments, resulting in updated second assignments. Further, the operations can include determining a revised assignment of root sequence indexes for the group of network equipment, wherein the revised assignment comprises the first assignments and the updated second assignments.

Facilitating assignment of root sequence indexes while minimizing network changes

Facilitating model-driven automated cell allocation in advanced networks (e.g., 5G and beyond) is provided herein. Operations of a method can comprise determining, by a system comprising a processor, a solution to an integer programming problem based on input data associated with a network inventory and configuration data for network devices of a group of network devices included in a communications network. Also, the method can comprise determining, by the system, respective cell identities and respective root sequence index assignments for the network devices. Further, the method can comprise implementing, by the system, a deployment of the respective cell identities and respective root sequence index assignments at the network devices.

Facilitating model-driven automated cell allocation in fifth generation (5G) or other advanced networks

A more efficient over-the-air software push can be facilitated by leveraging a smart scheduling system for vehicles. The smart scheduling system can use location and network capacity data to prioritize over-the-air software pushes for vehicles. For instance, a vehicle, which is only operational during off-peak wireless network hours can receive a software push during the off-peak times because wireless network capacity is not an issue. However, vehicles, which are used primarily during heavy peak wireless network times can receive software in a prioritized manner based on location data, frequency of use, network capacity, etc.

Facilitation of efficient software downloads for vehicles

Software downloads to Internet of things (IoT) devices are facilitated over a constrained network. In one embodiment a method comprises monitoring, by a network device comprising a processor, data determined to have been sent to a device for transmission to the device via a radio access network device of a wireless communication network, and determining, by the network device, a type of traffic associated with the data. The method further includes based on a determination that the data comprises firmware and that the type of traffic is of a traffic priority that is lower than a defined traffic priority, applying, by the network device, a low priority transport protocol to the data, wherein the applying comprises associating protocol information with the data representative of the low priority transport protocol.

Facilitating software downloads to internet of things devices via a constrained network

A method includes receiving, at a model and optimization framework, a request, rendered in a first format, for a service to be implemented on a subset of a software-defined network. The request includes at least one constraint affecting implementation of the service. The method includes producing an optimized solution, rendered in a second format, for implementing the service based on the at least one constraint. The method includes translating the optimized solution to the first format and providing instructions for a cloud manager to implement the service consistent with the translated optimized solution.

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Highland Park, NJ, United States of America

Carlos Eduardo De Andrade