At&t Mobility II LLC

AT&T Intellectual Property I, LP

Shomik Pathak

Zihui Ge

Ajay Mahimkar

Sarat Puthenpura

Iftekhar Alam

James P Daves

Kurt Donald Huber

David Lee Applegate

Yu Xiang

Xuan Liu

Sanjeev Ahuja

Nauman Shafi

Swati Roy

Yusef Shaqalle

Nemmara K Shankaranarayanan

Slawomir Mikolaj Stawiarski

Wenjie Zhao

Jennifer Yates

Wei Yuan

William D Turczyn

Harry Liu

Cecilia N Nguyen

Yang Wang

Mark Butler

Murari Lamsal

Jason E Carter

James Reichel

Changhan Ge

Joseph Maniaci

Rene Rivera, Jr

Rory Darwin Pinili

A processing system may obtain a data set with records of network parameter changes, each record including at least one network parameter change and at least one attribute associated with a first aspect of a communication network, and a corresponding network performance indicator change. A first record may include a plurality of network parameter change groups. The processing system may next perform a de-confusion process by identifying a second record comprising a single network parameter change group, determining that a corresponding network performance indicator change is different from that of the at least the first record, and updating the data set to replace the first record with at least two replacement records. The processing system may apply at least one of the network parameter change groups to a second aspect of the communication network based upon a decision output of a classifier that is trained using the updated data set.

Performance-driven network parameter changes in a communication network

A method performed by a processing system including at least one processor includes grouping a plurality of nodes of a telecommunications network into a plurality of reference groups, based on a plurality of configuration attributes and on a plurality of load, mobility, radio frequency attributes for the plurality of nodes, selecting a first reference group of the plurality of reference groups, where the first reference group includes a subset of the plurality of nodes, selecting a first configuration parameter of the first reference group to be tuned, identifying a first value for the first configuration parameter that is most prevalent among the subset of the plurality of nodes, and setting the first configuration parameter for all nodes in the subset of the plurality of nodes to the first value.

Optimization of long term evolution/fifth generation service through conformity-based recommendations

Aspects of the subject disclosure may include, for example, examining first actions imposed as part of a maintenance activity in respect of a communication network while the communication network is subjected to second actions as part of the maintenance activity, determining, based on the examining, that the first actions, in whole or in part, fail to adhere to a threshold, a requirement, or a specification, and based on the determining, reversing at least one action of the first actions. Other embodiments are disclosed.

Apparatuses and methods for facilitating a health check in respect of communication networks and systems, inclusive of an outer loop health check

A method includes selecting a study group including a first network element and a second network element of a network, selecting a control group including a third network element, identifying times at which a change is deployed at the first network element and the second network element, time-aligning the change at the first element and the change at the second network element to a common time, performing a statistical analysis that compares the performance of the network before the common time to the performance of the network after the common time, detecting an impact of the change on a performance of the network based on the statistical analysis, and initiating a remedial action when the impact comprises a degradation to the performance.

Assessing the impacts of cellular network changes

The technologies described herein are generally directed to facilitate allocating resources to zones for IOT equipment in a fifth generation (5G) network or other next generation networks. An example method discussed herein includes identifying, by carrier allocation equipment, carrier transmission information corresponding to transmission of a first carrier signal configured to support Internet of things equipment. The method can further comprise analyzing, by the carrier allocation equipment, the carrier transmission information to determine coverage information corresponding to a potential for coverage, by the first carrier signal, of an Internet of things equipment support zone corresponding to a geographic area. The method can further include, based on the coverage information, facilitating configuring transmission parameter information, representative of a transmission parameter applicable to the coverage of the Internet of things equipment support zone by the first carrier signal.

Allocating resources to internet of things equipment in a fifth generation (5G) network or other next generation networks

The technologies described herein are generally directed to configuring carrier aggregation zones based on transmission information in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include identifying, by carrier aggregation equipment including a processor, carrier transmission information corresponding to a first carrier signal and a second carrier signal. The method can further include analyzing, by the carrier aggregation equipment, the carrier transmission information to determine first overlap zone information representative of a first carrier overlap zone for the first carrier signal and the second carrier signal. Further, based on the first overlap zone information, the method includes facilitating configuring transmission parameter information representative of a transmission parameter applicable to transmission of the first carrier signal, to enable carrier aggregation by network equipment within the first carrier overlap zone.

Carrier aggregation configuration in fifth generation (5G) networks or other next generation networks

The described technology is generally directed towards automated cell parameter updates in cellular networks. A network automation platform architecture is disclosed which includes elements configured to automatically analyze, recalculate, and deploy cell parameters such as PCI and RSI parameters, for cells of a cellular communication network.

Automated cell parameter update in cellular networks

A method and apparatus for transmitting a control signal to a self organizing network controller, are disclosed. For example, the method implemented via a processor receives a change, determines whether the change to at least one network element maintains an acceptable level for one or more service quality metrics based on a predicted service quality metric calculated using a modeling function, generates a control signal based on the determining, and transmits the control signal to the self organizing network controller to cause the self organizing network controller to configure the at least one network element in a communication network in accordance with the control signal.

Method and apparatus for transmitting a control signal to a self organizing network controller

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Richardson, TX, United States of America

Shomik Pathak