At&t Intellectual Property I, LP

At&t Mobility Ii LLC

The University of Texas System

At+t Corp.

Sarat Puthenpura

Sam Houston Parker

Wenjie Zhao

David Gerald Belanger

Ravi Raina

Ioannis Broustis

Gopalakrishnan Meempat

Zihui Ge

Nemmara K Shankaranarayanan

Arun Jotshi

He Yan

Huahui Wang

Rittwik Jana

Mark D Austin

Carlos Eduardo De Andrade

Weiyi Zhang

Sheldon Kent Meredith

Jeremy Fix

Slawomir Mikolaj Stawiarski

Ajay Mahimkar

Leonid Razoumov

Syed Anwar Aftab

Yuning Yang

Raghvendra Savoor

Vijay Gopalakrishnan

Eric Rozner

Vaneet Aggarwal

Jie Chen

Stuart Blackburn

Wei Yuan

Vishwanath Ramamurthi

Shomik Pathak

Dipak Ghosal

Phyllis Weiss

Anthony Caracciolo

Will Adams Culpepper

Mira Desta

Brian Heard

Kurt Donald Huber

Jia Wang

Emir Halepovic

Rakesh Sinha

Matti A Hiltunen

Ankitkumar N Patel

Aman M Shaikh

David Lee Applegate

Abraham George

Mark Pack

David Kinsey

Abbas Munawar Fazal

Sastry Isukapalli

Marco Platania

Kyung-Wook Hwang

Xing Xu

Harold Orr McCaskey

Kathryn Childs

Robert Samson

Adrian Kester

Robert D Mathews

Swati Roy

Lynn Lopez

Jason Pipkin

Chichi Boderoux

Carla Sykes

Kevin Wilkins

Jennifer Yates

Lili Qiu

Simon Tse

Dongmei Wang

Kostas Oikonomou

Mubashir Adnan Qureshi

Xuan Liu

Giritharan Rana

Hauhui Wang

Kenneth G Kristiansen, Jr

Dave Belanger

Gopal Meempat

Ali Bastani

Nabeel Mir

A processing system may apply a data set comprising utilization metrics of a cells of a cell sector to a throughput prediction model to obtain a first predicted throughput for the cell sector for a designated future time period and for all cells being in an active state. The processing system may generate a first modified data set simulating a first cell being placed in an inactive state, by distributing utilization metrics of the first cell over at least one additional cell, and may apply the first modified data set to the throughput prediction model to obtain a second predicted throughput. The processing system may then determine that the second predicted throughput meets a threshold throughput that is based on the first predicted throughput, and transmit at least one instruction to place the first cell in the inactive state for the designated future time period in response to the determining.

Cell site energy utilization management

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

The described technology is generally directed towards cellular communication network sleep management. A central network automation platform can control sleep settings deployed to radio access network nodes that support the cells of the cellular communication network. The network automation platform can collect cell configuration data and can use the cell configuration data to determine sleep settings for sleep-eligible cells. The network automation platform can furthermore monitor performance of the sleep-eligible cells and neighbor cells to determine whether the sleep settings have led to degraded performance.

Cellular communication network sleep management

In one embodiment, a method includes receiving, by one or more interfaces, an optimization application, a request for an optimization, one or more policies required to implement the optimization, and data required to implement the optimization. The method also includes converting, by one or more processors and the optimization application, the one or more policies into optimization constraints and objective functions. The method further includes determining, by one or more processors and the optimization application, a solution to the optimization based on the optimization constraints, the objective functions, and the data.

Optimization application

The disclosed technology is directed towards automated configuration enforcement in wireless communication systems, which can be implemented in an audit manager (e.g. an ONAP micro-service) as part of an overall architecture. The audit manager obtains misconfiguration data representing misconfigured configurable parameter data of network elements of a radio access network, and corrective parameter data corresponding to the misconfiguration data. The audit manager issues a reconfiguration action to the network elements, which instructs the network elements to reconfigure their current configurable parameter data based on the corrective parameter data. Following reconfiguration, the audit manager operates to monitor the network elements performance, e.g., to check for performance degradation of a cell site relative to previous performance metrics.

Automated configuration enforcement in wireless communication systems

Aspects of the subject disclosure may include, for example, monitoring communication traffic in a communication network including a plurality of cell sites and a plurality of collector circuits over a time period, storing information about the communication traffic and information about available network resources in a database and predicting where additional capacity will be needed in the communication network at a future time, wherein the predicting is based on the information about the communication traffic. The subject disclosure may further include improving assignment of additional capacity in the communication network between the plurality of cell sites and the plurality of collector circuits based on the information about available network resources and communicating with network switches to assign the additional capacity within the communication network and initiate communication in the communication network. Other embodiments are disclosed.

Mobility backhaul bandwidth on demand

A method and apparatus for providing trouble isolation are disclosed. For example, the method monitors a plurality of sessions for a user group for detecting an abnormal cause code associated with the user group, determines a root cause for the abnormal cause code when a deviation is determined to have occurred for the cause code of the user group, wherein the root cause identifies either an issue associated with the communications network or an issue associated with user endpoint devices of the user group, and generates a ticket indicating the root cause.

Method and apparatus for providing trouble isolation via a network

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

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

Aspects of the subject disclosure may include, for example, embodiments include receiving a first trouble ticket reporting a first service outage of a communication network and a second trouble ticket reporting a second service outage of the communication network. Further embodiments can include determining a first customer impact according to the first service outage and determining a second customer impact according to the second service outage. The first customer impact is determined by a degradation of quality metrics, a site priority, and a number of complaints due to each service outage. Additional embodiments can include identifying that the second customer impact is higher than the first customer impact. Also, embodiments can include prioritizing a resolution of the second trouble ticket over the first trouble ticket according to the second customer impact being higher than the first customer impact. Other embodiments are disclosed.

Methods, devices, and systems for prioritizing mobile network trouble tickets based on customer impact

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

Growing community of inventors

Berkeley Heights, NJ, United States of America

Sarat Puthenpura