AT&T Intellectual Property I, LP

At&t Mobility II LLC

Yuning Yang

Wenjie Zhao

Sarat Puthenpura

Jie Chen

Mark D Austin

Nemmara K Shankaranarayanan

Slawomir Mikolaj Stawiarski

Sam Houston Parker

Carlos Eduardo De Andrade

Wei Yuan

Mark Pack

Huahui Wang

Ye Ge

Zhen Wan

Ke Zhang

Xidao Wen

Intelligent, automated, fixed wireless internet planning (e.g., using a computerized tool) is enabled. For instance, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: determining, for a user equipment determined to be within a defined coverage area, a signal to interference and noise ratio, based on the signal to interference and noise ratio, determining a spectral efficiency value corresponding to the user equipment, based on the spectral efficiency value and a total available bandwidth of a network via which the defined coverage area is enabled, determining an available throughput corresponding to the user equipment, and in response to a determination that the available throughput exceeds a threshold throughput, designating the user equipment, in a data store, as being covered within the defined coverage area by the total available bandwidth of the network.

Planning of fixed wireless internet

Facilitating analysis and resource planning for advanced heterogeneous networks (e.g., 5G, 6G, and beyond) is provided herein. A system is provided that includes a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can include determining that a resource is to be added to existing resources at a grid level of a heterogeneous network. Further, the operations can include selecting candidate locations for placement of the resource based on a coverage-driven objective and a capacity-driven objective defined for the heterogeneous network. The coverage-driven objective can be associated with a demand for services within the grid level of the heterogeneous network. The capacity-driven objective can be associated with demand growth within the grid level of the heterogeneous network. The resource can be a fifth generation millimeter wave node or a cloud radio access network node.

Facilitating heterogeneous network analysis and resource planning for advanced networks

Predicting small cell capacity and coverage to facilitate offloading of macrocell capacity is presented herein. A system selects a group of candidate locations for placement of respective small cells to facilitate offloading, via the respective small cells, of traffic from respective macrocells corresponding to the candidate locations&#x2014;the respective small cells including first transmission powers that are less than second transmission powers of the respective macrocells. Further, for each candidate location of the group of candidate locations, the system determines an estimated amount of traffic capacity of a small cell of the respective small cells that has been presumed to have been placed at the candidate location, and determines estimated signal strengths of respective signals that have been predicted to have been received from the small cell at respective portions of a grid of a defined signal coverage area corresponding to the candidate location.

Predicting small cell capacity and coverage to facilitate offloading of macrocell capacity

The technologies described herein are generally directed to facilitating operation of a system for implementing fifth generation (5G) or other next generation networks. In accordance with one or more embodiments, a method described herein can include identifying, by a device comprising a processor, predicted resource usage of a first antenna covering a geographic zone. Further, the method can include selecting, by the device, a group of geographic siting locations within the geographic zone for potentially siting ones of a group of second antennas. In addition, selecting, by the device, a spatial arrangement in relation to the first antenna, of a subset of the group of geographic siting locations can occur, with a selected spatial arrangement including an arrangement to maintain the predicted resource usage in the condition.

Densifying a mobility network by siting antennas with demand zone planning, in a communications network

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

An architecture for providing indoor planning data that can be used to increase the density of mobility networks. A method can comprise receiving, via network equipment, resource exhaustion data indicative of radio spectrum usage within a determined bin representing a defined geographic area; receiving, via the network equipment, traffic data associated with the determined bin, wherein the traffic data indicates a volume of data flowing into the determined bin; receiving, via the network equipment, coverage data representing a quality of service metric data indicative of a determined reference signal receive power metric; and based on applying a ranking process to the resource exhaustion data, the traffic data, and the coverage data, generating a ranked list of structures, wherein a structure of the ranked list of structures includes the determined bin.

Indoor planning for densifying mobility networks

A network device that determines RACH root sequences for AP devices within a given market. The network device can utilize a specialized graph coloring process or algorithm that has been adapted to, e.g., ensure that no two neighboring AP devices share the same RACH root sequences, provided certain additional constraints not found in graph coloring theory are met. For example, AP devices can have multiple RACH root sequence, whereas in traditional graph coloring problems, each vertex typically has only one color.

Determining RACH root sequences as a function of access point configuration using graph coloring

In one embodiment, a method includes receiving, by one or more interfaces, a location of a physical object and receiving, by the one or more interfaces and from a data platform, an image associated with the physical object and the location of the physical object. The method also includes extracting, by one or more processors and from the image, an attribute associated with a feature of the physical object and classifying, by one or more processors, the attribute, wherein classifying the attribute comprises associating the attribute with a characteristic of the feature of the physical object. The method further includes classifying, by the one or more processors, the physical object and determining, by the one or more processors, to identify the physical object as eligible for modification.

System and method for classifying a physical object

Scrambling code conflicts can be mitigated by primary scrambling code reuse that minimizing a potential interference Primary scrambling codes are applied to a first set of cells located in a portion of the network being considered. A second set of cells are evaluated for primary scrambling code reuse based on a distance parameter and/or a coverage area. If the distance parameter is greater than a defined distance, primary scrambling code reuse can be applied. If all distance parameters evaluated are less than the distance parameter, a length of the distance parameter is reduced and the distance between cells is reevaluated.

Growing community of inventors

Basking Ridge, NJ, United States of America

Yuning Yang