AT&T Intellectual Property I, LP

Dhruv Gupta

Rittwik Jana

Jin Wang

Rajarajan Sivaraj

Zhengye Liu

Weihua Ye

Subhabrata Bapi Sen

Varun Gupta

Laurie Bigler

Bharath Balasubramanian

Kittipat Apicharttrisorn

Mostafa Tofighbakhsh

Yongdong Zhao

Deva-Datta Sharma

Shraboni Jana

Xiaojun Tang

Zuxian Guo

Dongho Kim

The technologies described herein are generally directed to modeling radio wave propagation in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include, for a network application, identifying, by a system comprising a processor, a characteristic value of a performance characteristic associated with an uplink connection enabled via a network of a user equipment to application server equipment hosting the network application. The method can further include, based on the characteristic value and a criterion, selecting, by the system, a first packet size for the uplink connection. The method can further include communicating, by the system, to the user equipment, the first packet size for use with the uplink connection.

Streaming augmented reality data in a fifth generation (5G) or other next generation network

The described technology is generally directed towards reducing latency in a wireless communications network. Radio access network latency data corresponding to a measured latency impact criterion is obtained by a network device of a wireless network. Based on the radio access network latency data, latency guidance data usable by the radio network device to achieve a reduction in communication latency that is experienced by a user equipment is predicted, e.g., by a learned model. The latency guidance data can be used to facilitate a reduction in the communication latency that is experienced by a user equipment.

Latency prediction and guidance in wireless communication systems

The described technology is generally directed towards generating and deploying radio access network parameters by a management platform. The management platform can use a defined data structure as a carrier for parameters within the management platform. Newly generated parameters can be placed in the defined data structure. The newly generated parameters can be approved at the management platform for deployment to radio access network devices. A software defined networking function of the management platform can convert a parameter into a specific format utilized at a radio access network, and the software defined networking function can deploy the converted parameter to the radio access network.

Automated parameter deployment for cellular communication networks

The described technology is generally directed towards machine learning deployment in radio access networks. A machine learning deployment pipeline can comprise a machine learning model design platform, a network automation platform, and a radio access network. Machine learning models can be designed at the machine learning model design platform, trained at the network automation platform, and deployed and used at the radio access network. The technology includes operations performed at each stage of the deployment pipeline in order to deploy machine learning models.

Machine learning deployment in radio access networks

The described technology is generally directed towards network slice management. A mobile communications network can comprise multiple sub-networks, namely, as an access network, a transport network, and a core network. Access network resources can be managed according to the techniques provided herein to meet service level agreement (SLA) commitments associated with network slices. Furthermore, resources of any sub-network can be managed in a manner that accounts for constraints imposed by the other sub-networks.

Network slice management

Aspects of the subject disclosure may include, for example, a system that provides for obtaining, by a server comprising a processor, network parameter data from an eNodeB of a wireless network. The server can determine a predicted bandwidth for a group of end user devices in a coverage area of the eNodeB according to the network parameter data. The server can receive, from an end user device of the group of end user devices, a request for the predicted bandwidth. The server can provide the predicted bandwidth to the end user device. The providing of the predicted bandwidth enables the end user device to provide a video chunk request to a content server that is based on a selected chunk delivery schedule and facilitates streaming of video content to the end user device from the content server. Other embodiments are disclosed.

Apparatus and method of video streaming

Growing community of inventors

San Ramon, CA, United States of America

Dhruv Gupta