T-mobile Usa, Inc.

T-mobile Innovations LLC

Yasmin Karimli

Gunjan Nimbavikar

Ming Shan Kwok

Wafik Abdel Shahid

Thomas P Lucht

Wei-Ming Lan

Hsin-Fu Henry Chiang

Adnan Rahat

Puja Gupta

Jie Hui

Shelby Seward

Antoine T Tran

Phani Ramisetty

Hsin Fu Henry Chiang

Cristian Asandului

Syed Toaha Ahmad

Joshua Finger

Mathew George

Senthil Kumar Mulluppadi Velusamy

Brian Allan Olsen

Neng-Tsann Ueng

Poornima Magadevan

Lin Zhang

Ryan Christopher Lindstrom

Carl Williams

Anthony J Silveira

Dinesh Arcot Kumar

Hassan Raza

Jeffrey Song

Kimberly Bylund

Pablo Tapia

Samir M Hodroj

Karthik Iyer

Boua Lor

Arturo Silis

Wafik Abdelshahid

ChihChin Wu

Ryan Cyrus Khamneian

Systems, devices, and techniques described herein relate to handover between Non-Standalone (NSA) and Standalone (SA) networks. An example method includes receiving, from a User Equipment (UE), a measurement report indicating that a signal threshold has been satisfied. In response to receiving the measurement report, handover of a communication session from a first core network to a second core network may be initiated. A message confirming that the communication session has been handed over from the first core network to the second core network can be received. In response to receiving the message, handover of the communication session can be initiated between a single radio bearer associated with a first Radio Access Technology (RAT) and a dual radio bearer associated with the first RAT and a second RAT.

Handover between non-standalone and standalone networks

Techniques for enabling real time translation and transcription services between users that have contacted emergency services and PSAP operators who are coordinating the emergency services are discussed herein. For example, a system determines that the user and the PSAP operator speak different language, are unable to effectively hear each other, or are otherwise struggling to communicate effectively. The system can determine that an augmentation of the communication session is to be provided and can initiate translation or transcription services via network edge computing resources. The network edge computing resources are configured to generate the augmented communication data and enable the communication network to merge the augmented communication data and the original communication data in real time.

Emergency session translation and transcription via audio forking and machine learning

Spatial compute devices form an ad-hoc communication network that comprises includes point-to-point wireless links between the spatial compute devices. The spatial compute devices exchange application data for a user application over the ad-hoc communication network. The spatial compute devices indicate their ad-hoc communication network, user application, and application users to an application server system. A new spatial compute device detects the ad-hoc communication network and queries the application server system. The application server system responds to the new device with the application users that are currently using the user application over the ad-hoc communication network. The new device presents to a user the application users that are currently using the user application over the ad-hoc network, and in response, receives a user selection of the user application. The new device wirelessly exchanges new application data for the user application with the other application users over the ad-hoc communication network.

User application delivery over ad-hoc wireless communication networks

Described herein are techniques, devices, and systems for selectively using a music-capable audio codec on-demand during a communication session. A user equipment (UE) may adaptively transition between using a first audio codec that provides a first audio bandwidth and a second audio codec (e.g., the EVS-FB codec) that provides a second audio bandwidth that is greater than the first audio bandwidth. The transition to the second audio codec may occur in response to determining that sound in the environment of the UE includes frequencies outside of a range of frequencies associated with a human voice, such as by determining that music is being played in the environment of the UE, which allows for selectively using a music-capable audio codec when it would be beneficial to do so.

Machine learning-based audio codec switching

Methods and systems are provided for differentiating an XR device from other devices when initiating communications with an Internet gateway. Initially the XR device sends a probe request to the Internet gateway, where the probe request includes an identifier of the device type and network requirements of the XR device. It is determined that the particular device type and network requirements in the probe request are supported by the Internet gateway, and is communicated to the XR device in the form of a probe response. A communication session is set up between the XR device, the Internet gateway, and a network, the communication session including QoS control based on the device type and network requirements of the XR device.

Extended reality device identification by internet gateway

Described herein are techniques, devices, and systems for training a machine learning model(s) and/or artificial intelligence algorithm(s) to determine where a mobile device (and, hence, a user of the mobile device) is located based on audio data associated with the mobile device and/or contextual data associated with the mobile device. The machine learning techniques may be used to determine contextual information about users, such as determining that a particular location is likely to be a user's home, office, or the like, based on movement patterns exhibited in the data associated with a user's mobile device. Once trained, the machine learning model(s) is usable to classify a mobile device as having been located at one of multiple candidate locations, such as indoors or outdoors, at a particular time. The described techniques can improve the accuracy of determining a mobile device's location, among other technical benefits.

Using machine learning to locate mobile device

Network scheduling in unlicensed spectrums is described. A scheduler can assess, at a first time, first conditions of an unlicensed spectrum, such as Long-Term Evolution unlicensed spectrum. Based at least partly on the first conditions, the scheduler can determine a schedule for transmitting data via the unlicensed spectrum. The scheduler can, for a first period of time, facilitate transmission of data between mobile devices that are communicatively coupled to the communication tower based at least partly on the schedule. At a second time after the first time, the scheduler can assess second conditions of the unlicensed spectrum and update the schedule based at least partly on the second conditions to generate an updated schedule. For a second period of time, the scheduler can facilitate transmission of data between the mobile devices based at least in part on the updated schedule.

Network scheduling in unlicensed spectrums

A telecommunication network associated with a wireless telecommunication provider can be configured to dynamically switch the primary cell (PCell) used by user equipment (UE) for carrier aggregation (CA) in 5G cellular networks. Instead of remaining anchored to an initially selected PCell, a different PCell may be dynamically selected based on different network conditions. The network conditions may include network congestion, network capacity, uplink speed, location of the UE, an activity of the UE (e.g., is the UE uploading or planning to upload data), and the like. As an example, the PCell may be selected from an n41 (2.5 GHz) cell and an n71 (600 MHz) cell. When the UE is close to the n41 cell, the n41 cell may be selected. When the UE is moving away from the cell center and toward the cell edge, the PCell may be switched from the n41 cell to the n71 cell.

Dynamically changing the primary cell (PCell) for fifth generation (5G) carrier aggregation

A network base station can select, for each of one or more attached terminals, a respective downlink transmission mode (DTM) based at least in part on respective channel condition information (CCI). The base station can determine a subframe allocation of DTMs to subframes of a radio frame, and transmit downlink data to terminals based the subframe allocation. Additionally or alternatively, the base station can receive load information from a second base station associated with a different access network and determine the subframe allocation based on the load information. The subframe allocation can associate a specific access network with each subframe. Additionally or alternatively, the base station can send the subframe allocation to the second base station. Additionally or alternatively, the base station can determine a proportion of GBR traffic of a particular DTM, determine a reference-signal transmission rate associated with that DTM, and transmit reference signals accordingly.

Wireless telecommunication using subframes

A fifth generation (5G) network can provide Rich Communication Services (RCS) between multiple user equipment (UE) of different device types and/or different operating systems. An RCS server in an IP Multimedia Subsystem (IMS) can be used to format data associated with a chat between UEs having different operating systems. The RCS server can initiate, establish, maintain, format, augment, or otherwise determine a rich communication chat message between the UEs over the 5G network.

Enhancements to rich communication group messaging based on operating system

Systems, methods, and devices can be used to perform service-aware utilization of shared wireless resources. In various instances, a quality metric (e.g., a Bit Error Rate (BER)) of a shared wireless resource is determined. Requested services can be provided based at least partly on the quality metric and the type of services being requested. For example, when the quality metric exceeds a threshold and the requested services are for Quality of Service (QoS)-sensitive services (e.g., Guaranteed Bit Rate (GBR) services), the services can be provided at least partially via a licensed wireless resource. However, when the quality metric does not exceed the threshold, and/or the requested services are for non-QoS-sensitive services (e.g., non-GBR services), the services can be provided via the shared wireless resource.

Service-aware utilization of shared wireless resources

The disclosure herein describes the use of a policy server to enable management of activation of an adaptively branded device with a brand with which the device is compatible. A brand registration is received from a device registration entity including a device identifier of a device and a registered brand identifier of a brand. The device identifier and registered brand identifier are linked in a data store. A device activation request is received from the device including the device identifier and a SIM brand identifier of a SIM installed in the device. Based on the SIM brand identifier differing from the registered brand identifier, activation of the device with the brand is prevented. Based on the SIM brand identifier matching the registered brand identifier, the device is activated with the brand of the registered brand identifier, whereby the device is enabled to complete a branding process based on the brand.

Managing activation and branding of an adaptively branded device

Techniques directed to utilizing networked devices to enhance user experience are described. In an example, sensor data received from sensor(s) associated with a facility can be used to determine a presence of a user equipment (UE) within or proximate to the facility. Based at least in part on the sensor data, an account associated with the UE can be accessed. Based at least in part on account data associated with the account, a distribution of network technology can be modified to temporarily enable the UE to access a service that is not otherwise available to at least one other UE within or proximate to the facility.

Enhanced algorithms for improved user experience using internet of things sensor integration

Growing community of inventors

Kirkland, WA, United States of America

Yasmin Karimli