Google Inc.

Qualcomm Incorporated

Jibing Wang

Erik Richard Stauffer

Tamer Adel Kadous

Aamir Akram

Ashok Mantravadi

Joel Linsky

Pranav Dayal

Roberto Rimini

Gwendolyn Denise Barriac

Dhananjay Ashok Gore

Hemanth Sampath

Brian Clarke Banister

Ahmed Kamel Sadek

Rashid Ahmed Akbar Attar

Yi Hsuan

Tibor Boros

Peter S Gaal

Yan Zhou

Alexei Yurievitch Gorokhov

Taesang Yoo

Rajat Prakash

Alireza Raissinia

Joseph Patrick Burke

Mitchell D Trott

Guido Robert Frederiks

Sharath Ananth

James Peroulas

Qin Zhang

Dieter W Statezni

Sathish Karunakaran

Tao Luo

Jing X Sun

Wooseok Nam

Yongbin Wei

Muhammad Sayed Khairy Abdelghaffar

Sharad Deepak Sambhwani

Jilei Hou

Yanjun Sun

Yong Li

Sandip HomChaudhuri

James Simon Cho

Zhanfeng Jia

Vijay L Asrani

Olaf Josef Hirsch

Richard Neil Braithwaite

Geert Arnout Awater

Shu Du

Srenik Suresh Mehta

Xiaoyin He

Shivank Nayak

Yibo Jiang

Neil Hendin

Iwen Yao

Brian George

Arunkumar Jayaraman

Pavan Nuggehalli

Eric Y Tsou

Weihong Jing

John R Saad, Jr

Sandip Homchaudhuri

Shihhao Chiu

Chien Pin Chiu

Pavan Huggehalli

Nicholas M McDonnell

Shiangrung Ye

Joel Benjamin Linsky

Erik Richard Staufer

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.

Dynamic codebooks for active coordination sets

In a radio access network (RAN), a method for increasing network efficiency includes partitioning a pool of identifiers into at least a plurality of identifier sets associated with a plurality of respective sets of base stations in the RAN. The identifier sets include a first identifier set associated with a first base station set of the plurality of respective sets of base stations. The method also includes determining that the first base station set is to serve a user device and assigning a first identifier from the first identifier set to the user device. The method further includes transmitting the first identifier to the user device and jointly transmitting, by at least two base stations of the first base station set, information to the user device via a channel, including using the first identifier to indicate that the channel carries information for the user device.

User equipment identity management for coordinating base stations

Methods, devices, systems, and means for coordinating full-duplex communication are described in which a user equipment, UE, configured as a coordinating user equipment for a user equipment-coordination set, UECS, selects a first subset of UEs in the UECS to jointly receive downlink signals and selects a second subset of UEs in the UECS to jointly transmit uplink signals. The coordinating UE receives uplink data to transmit to the network entity and receives, from the first subset of UEs, demodulated and sampled downlink data that is received concurrently with joint-transmission of the uplink data. The coordinating UE combines the samples received from each UE in the first subset of UEs and jointly processes the combined samples to provide decoded data using the received uplink data to cancel crosstalk of uplink signals for the transmitted uplink data to downlink signals to the received downlink data.

User equipment-coordination set full-duplex communication

This document describes techniques and devices for determining a machine-learning architecture. A user equipment (UE) transmits, to a network-slice manager of a wireless network, a first machine-learning architecture request message to request permission to use a first machine-learning architecture. The UE receives a first machine-learning architecture response message that grants permission to use the first machine-learning architecture based on a first network slice, the first machine-learning architecture forming a portion of at least one first end-to-end machine-learning architecture associated with the first network slice, the at least one first end-to-end machine-learning architecture being a distributed machine-learning architecture that is configured to process wireless communication signals and is formed by the first machine-learning architecture implemented by the user equipment, a machine-learning architecture implemented by a base station, and a machine-learning architecture implemented by an entity of a core network. The UE wirelessly communicates data using the first machine-learning architecture.

Determining a machine-learning architecture for network slicing

Techniques and apparatuses are described for joint-processing of random access channel communications to improve the reliability and/or the geographic range of a random access procedure for a user equipment. Joint-processing, including joint-transmission and/or joint-reception, by a user equipment-coordination set on behalf of a single user equipment, or by an Active Coordination Set of base stations with the single user equipment, can improve the link budget of Random Access Channel communications and facilitate network access for a user equipment at a greater distance from a base station or in the face of challenging channel conditions.

Joint-processing of random access channel communications

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.

Facilitating self-organizing network (SON) enhancement

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.

Multi-adaptive phase-changing device communications

Techniques and apparatuses are described for determining a position of user equipment by using adaptive phase-changing devices. In aspects, a base station transmits wireless signals for a UE toward respective reconfigurable intelligent surfaces (RISs) of adaptive phase-changing devices. The APDs may direct reflections of the wireless signals in a direction, such as toward the UE, based on a configuration of the RIS of the APD. The base station receives, from the UE via a wireless connection, identifiers of the reflections of the wireless signals that are received by the UE. In some cases, the base station also receives a signal quality parameter associated with the reflection reaching the UE. The base station determines angular information based on the respective identifiers and/or signal quality parameters of the reflections. Based on the angular information and known positions of the APDs, the base station determines a position of the UE.

Determining a position of user equipment by using adaptive phase-changing devices

A user equipment (UE) manages thermal levels of antenna modules with reference to a temperature threshold. The UE includes multiple antenna modules having a first antenna module and a second antenna module and at least one wireless transceiver coupled to the multiple antenna modules. The UE also includes a processor and memory system implementing an antenna module thermal manager. The manager is configured to obtain a first temperature indication corresponding to the first antenna module of the multiple antenna modules. The manager is also configured to perform a comparison of the first temperature indication to at least one temperature threshold. The manager is further configured to switch, based on the comparison, from using the first antenna module to using the second antenna module for wireless communication with the at least one wireless transceiver.

Thermal management with antenna modules

Techniques and apparatuses are described for user equipment-coordination set, UECS, hybrid automatic repeat request, HARQ, that establish a HARQ timeline that is specific to the capabilities of a respective UECS. Compared to a single user equipment, UE, the HARQ timeline for a UECS depends on a number of factors, such as the joint processing capability in the UECS, latency of communication over a local wireless network between the UEs in the UECS, or the like. Based on its capabilities, the UECS can request uplink and/or downlink processing delay times or a UECS-specific HARQ timeline from a base station. The base station grants the uplink and/or downlink processing delay times or the UECS-specific HARQ timeline to the UECS in a layer-1, layer-2, or a layer-3 control message. The use of a UECS-specific HARQ timeline increases the reliability of HARQ signaling for uplink and downlink communication between the UECS and a base station.

User equipment-coordination set hybrid automatic repeat request

In aspects, a non-terrestrial communication system communicates with a user equipment, UE, using repetitive communications. The non-terrestrial communication system determines a repetition configuration for repetitive communications with the UE and indicates the repetition configuration to the UE. The non-terrestrial communication system communicates with the UE using the repetitive communications in accordance with the repetition configuration.

Repetitive communications for improving reliability of satellite communications

Processing hardware in a user equipment (UE) that communicates with a core network (CN) via a radio access network (RAN) can implement a method of routing outgoing traffic from the UE. The method includes determining a proscribed traffic descriptor which the UE is to match to rules defined by the CN that specify route selection for outgoing traffic (). The method further includes preventing use of the traffic descriptor for application of a rule specifying route selection to the outgoing traffic ().

Applying rules for routing outgoing traffic at a user device

Techniques and apparatuses are described for deep neural network (DNN) processing for a user equipment-coordination set (UECS). A network entity selects () an end-to-end (E2E) machine-learning (ML) configuration that forms an E2E DNN for processing UECS communications. The network entity directs () each device of multiple devices participating in an UECS to form, using at least a portion of the E2E ML configuration, a respective sub-DNN of the E2E DNN that transfers the UECS communications through the E2E communication, where the multiple devices include at least one base station, a coordinating user equipment (UE), and at least one additional UE. The network entity receives () feedback associated with the UECS communications and identifies () an adjustment to the E2E ML configuration. The network entity then directs at least some of the multiple devices participating in an UECS to update the respective sub-DNN of the E2E DNN based on the adjustment.

Deep neural network processing for a user equipment-coordination set

The techniques described in this disclosure enhance the reception of the information at a user equipment (UE) from an Active Coordination Set (ACS) of a wireless network system, where the ACS includes a set of base stations that jointly operate to communicate data between the system and the UE. The ACS allocates respective subsets of a plurality of time domain resources for use by corresponding ACS base stations in communicating with the UE, and provides, to the UE, an indication of the allocations. Based on the received allocation indication (), the UE obtains data payload transmitted by the ACS () by tuning to various base stations in accordance with their respective time domain resource allocations (). The ACS may redundantly transmit selected data payload and/or employ different encoding schemes to further enhance reception of information at the UE.

Enhancing performance of downlinks between user equipment and active coordination sets

Techniques and apparatuses are described for machine-learning architectures for broadcast and multicast communications. A network entity processes broadcast or multicast communications using a deep neural network (DNN) to direct the one or more broadcast or multicast communications to a targeted group of user equipments (UEs) using the wireless communication system. The network entity receives feedback from at least one user equipment (UE) of the targeted group of UEs. The network entity determines a modification to the DNN based on the feedback. The network entity transmits an indication of the modification to the targeted group of UEs. The network entity updates the DNN with the modification to form a modified DNN. The network entity processes the broadcast or multicast communications using the modified DNN to direct the broadcast or multicast communications to the targeted group of UEs using the wireless communication system.

Machine-learning architectures for broadcast and multicast communications

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Saratoga, CA, United States of America

Jibing Wang