Abstract: A UE receives satellite ephemeris information from a server server and calculates one or more satellite parameters for at least one satellite based on the satellite ephemeris information. The UE controls, based on the one or more satellite parameters, operation of a software application that utilizes a data service provided via the at least one satellite. Calculating satellite parameters can include calculating one or more satellite parameters at an API of the UE, and controlling operation of a software application can include receiving, at the API, a request for satellite ephemeris information from the software application, providing, by the API, a representation of the one or more satellite parameters to the software application in response to the request, and adjusting, at the software application, an operation of the software application based on the received representation of the one or more satellite parameters.

Abstract: This document describes techniques and apparatuses for distributed network cellular identity management. In particular, a distributed-network cellular-identity-management (DNCIM) server includes a lookup table that stores and relates together a user-equipment (UE) public key associated with a UE private key, a core-network (CN) public key associated with a CN private key, and a subscriber identity. Using the DNCIM server, the UE and an authentication server respectively generate two different (e.g., asymmetric) cipher keys based on the UE private key and the CN public key, and the UE public key and the CN private key. The UE and the authentication server can also authenticate one another by referencing information in the lookup table of the DNCIM server. Using these cipher keys, the UE and the authentication server can establish secure communications with each other.

Abstract: Techniques and apparatuses are described for enabling base station-user equipment messaging regarding deep neural networks. A network entity (base station 121, core network server 320) determines a neural network formation configuration (architecture and/or parameter configurations 1208) for a deep neural network (deep neural network(s) 604, 608, 612, 616) for processing communications transmitted over the wireless communication system. The network entity (base station 121, core network server 302) communicates the neural network formation configuration to a user equipment (UE 110). The user equipment (UE 110) configures a first neural network (deep neural network(s) 608, 612) based on the neural network formation configuration. In implementations, the user equipment (UE 110) recovers information communicated over the wireless network using the first neural network (deep neural network(s) 608, 612).

Abstract: In aspects, a base station determines to transmit a multi-user-equipment communication, multi-UE communication, to multiple user equipments, UEs. The base station determines to include an adaptive phase-changing device, APD, in a communication path for a wireless signal carrying the multi-UE communication and selects a surface configuration for a surface of the APD based on determining to transmit the multi-UE communication. The base station directs the APD to apply the surface configuration to the surface and transmits the wireless signal carrying the multi-UE communication by transmitting the wireless signal towards the surface of the APD.

Abstract: The invention relates to a topical composition having a low anionic surfactant amount for use in the treatment and/or prevention of a corona virus infection.

Abstract: In aspects, a base station establishes a wireless connection with a user equipment, UE. The base station determines to include at least a first adaptive phase-changing device, APD, and a second APD in a wireless communication path with the UE. In response to determining to include multiple APDs in the communication path, the base station determines a first surface configuration for a first surface of the first APD and a second surface configuration for a second surface of the second APD. The base station directs the first APD to apply the first surface configuration to the first surface and directs the second APD to apply the second surface configuration to the second surface. The base station and the UE communicate with the UE using wireless transmissions that travel along a wireless communication path that includes the first surface of the first APD and the second surface of the second APD.

Abstract: A method includes receiving an information block as an input to a transmitter neural network, receiving, as an input to the transmitter neural network, sensor data from one or more sensors, processing the information block and sensor data at the transmitter neural network to generate an output, and controlling an RF transceiver based on the output to generate an RF signal (134) for wireless transmission. Another method includes receiving a first output from an RF transceiver as a first input to a receiver neural network, receiving, as a second input to the receiver neural network, a set of sensor data from one or more sensors, processing the first input and the second input at the receiver neural network to generate an output, and processing the output to generate an information block representative of information communicated by a data sending device.

Abstract: Techniques described herein describe aspects of signal adjustments in user equipment-coordination set, UECS, joint transmissions. A base station analyzes a first joint transmission from multiple user equipments, UEs, participating in a UECS, where the multiple UEs include a coordinating UE of the UECS and at least one non-coordinating UE participating in the UECS. The base station determines that the first joint transmission fails to meet a performance metric and directs the multiple UEs participating in the UECS to add signal adjustments to a second joint transmission.

Abstract: This document describes techniques and systems that enable a base-station-initiated grant revoke. The techniques and systems allow a base station to generate and send a grant-revocation message (GRM) to a user equipment (UE) to revoke a scheduled uplink (UL) or downlink (DL) grant from the base station. The base station can transmit the GRM to the UE using a variety of techniques. For example, based on a trigger event, the base station may assign a UE identifier, such as a specific radio network temporary identifier (RNTI), to the UE and transmit the GRM to the UE using a revoke-physical-downlink-control-channel (R PDCCH) transmission that is associated with the UE identifier. These techniques allow the base station to revoke a scheduled UL or DL grant, which can enable the UE to quickly address timing-critical resource allocation issues and mitigate the effects of adverse operating conditions.

Abstract: This document describes techniques and apparatuses for a user-equipment-coordination set for disengaged mode. In aspects, a base station forms a disengaged-mode-user-equipment coordination set including multiple user equipment operating in a disengaged mode. The disengaged-mode-user-equipment coordination set uses joint transmission and reception to communicate with the base station. The base station communicates control-plane information to an individual user equipment or multiple user equipment in the disengaged-mode-user-equipment coordination set.

Abstract: The present disclosure describes systems and methods for a user equipment wirelessly communicating with another user equipment using dual connectivity (DC) with a terrestrial base station and a satellite or high-altitude platform. The described methods and systems include a principal routing manager assessing that different subsets of data, to be transmitted from the user equipment to the other user equipment, can use different, respective qualities of service (QoS) offered through different wireless-communication networks associated with the terrestrial base station and the satellite or high-altitude platform.

Abstract: In some implementations, a user device may present, on a user interface (UI) associated with a user account, a first UI element. The user device may detect a user interaction, with the user device, indicating the first UI element. The user device may present, on the UI and based on the user interaction, a second UI element, which increases in length in a first direction from a start point, along a closed path around the first UI element and ending at the start point, while the user interaction is being detected. The second UI element completes the closed path when an interaction duration satisfies a duration threshold, which indicates a confirmation of a request for an action associated with the user account. The duration threshold is based on one or more parameters associated with one or more of the action, the user account, or the user interaction.

Abstract: This document describes methods, devices, systems, and means for determining a joint-codebook for wireless communication with a user equipment, UE, by a base station in an active coordination set, ACS, in which a base station receives capability information from one or more other base stations in the ACS. The base station generates a joint-codebook for the ACS based on the received capability information and sends the joint-codebook to the one or more other base stations in the ACS. The base station and the other base stations in the ACS jointly-transmit the joint-codebook to the UE and receive Precoding Matrix Indicator, PMI, feedback from the UE. The base station and the other base stations in the ACS jointly-process downlink data for the UE using the PMI feedback and the joint-codebook and jointly-transmit the downlink data to the UE.

Abstract: Wireless networks may have thousands of configurable parameters, so manual tuning is infeasible. A self-organizing network (SON) can provide automation. However, automated algorithms are not designed to interact with a wireless network, and network experimentation can jeopardize reliability. To address the former, a SON facilitator of a wireless network management node exposes an API that can translate network configuration information for consumption by a SON enhancer, which may implement an AI algorithm for network tuning. The SON facilitator can also transform output from the SON enhancer into directions for controlling a test scenario, including generating a DL SON message describing the test to a UE. To further increase reliability during the test scenario, the UE can be provisioned with two wireless connections. A first connection is unchanged by the test scenario for stability, and a second connection is used for testing.

Abstract: Techniques and apparatuses are described for enabling base station-user equipment messaging regarding deep neural networks. A network entity (base station 121, core network server 320) determines a neural network formation configuration (architecture and/or parameter configurations 1208) for a deep neural network (deep neural network(s) 604, 608, 612, 616) for processing communications transmitted over the wireless communication system. The network entity (base station 121, core network server 302) communicates the neural network formation configuration to a user equipment (UE 110). The user equipment (UE 110) configures a first neural network (deep neural network(s) 608, 612) based on the neural network formation configuration. In implementations, the user equipment (UE 110) recovers information communicated over the wireless network using the first neural network (deep neural network(s) 608, 612).

Abstract: Techniques and apparatuses are described for adaptive phase-changing device power-saving operations. In aspects, a base station determines to transition an adaptive phase-changing device (APD) into an enabled APD-PS mode and determines an APD-PS configuration for the APD that specifies a framework for operating in the enabled APD-PS mode. The base station then directs the APD to operate in the enabled APD-PS mode by communicating the APD-PS configuration to the APD and transmits or receives wireless signals using a surface of the APD and based on the APD-PS configuration.

Abstract: A computing device is described for performing local interference avoidance, when supporting concurrent voice and data transmissions, and with access to multiple radios. The computing device predicts when coexistence issues will occur from maintaining independent voice and data transmissions using separate radios. To avoid local interference issues, the computing device automatically switches to operating a different combination of radios, making local interference less likely to occur. In some cases, the computing device may consolidate the voice and non-voice data exchanges to occur using a single radio. In some cases, rather than consolidation, the computing device may move the voice or the non-voice data exchange to a different radio as a way to avoid the local interference.

Abstract: The present disclosure describes systems and methods for a user equipment wirelessly communicating with another user equipment using dual connectivity (DC) with a terrestrial base station and a satellite or high-altitude platform. The described methods and systems include a principal routing manager assessing that different subsets of data, to be transmitted from the user equipment to the other user equipment, can use different, respective qualities of service (QoS) offered through different wireless-communication networks associated with the terrestrial base station and the satellite or high-altitude platform.

Abstract: Techniques and apparatuses are described for integrated access backhaul with an adaptive phase-changing device (APD) are described. In aspects, a donor base station determines to include an APD in a communication path for the wireless backhaul link with a node base station and apportions APD access to the APD for the node base station. The donor base station then communicates with the node base station using the surface of the APD and based on the apportioned APD-access by using the surface to exchange wireless signals with the donor base station.

Abstract: Techniques and apparatuses are described for machine-learning architectures for broadcast and multicast communications. In implementations, a network entity determines a configuration of a deep neural network (DNN) for processing broadcast or multicast communications transmitted over a wireless communication system, where the communications are directed to a targeted group of user equipments (UEs). The network entity forms a network-entity DNN based on the determined configuration of the DNN and processes the broadcast or multicast communications using the network-entity DNN. In implementations, the network entity forms a common DNN to process and/or propagate the broadcast or multicast communications to the targeted group of UEs.