Google Inc.

Broadcom Corporation

Avago Technologies General IP (singapore) Pte. Ltd.

Avago Technologies International Sales Pte. Limited

Erik Richard Stauffer

Jibing Wang

Aamir Akram

Kamlesh Rath

Bertrand Martyn Hochwald

Soumen Chakraborty

Manish Airy

Rishi Ranjan

Djordje Tujkovic

Leena Zacharias

Sharath Ananth

Robert G Lorenz

Stephanie Felicia Pereira

Richard Compton

Kamesh Medapalli

Sriram K Rajagopal

Paul Heninwolf

Qin Zhang

William Charles Fujimoto

Sathish Karunakaran

Sam Padinjaremannil Alex

Vijay L Asrani

Gowrisankar Somichetty

Shashidhar Vummintala

Srenik Suresh Mehta

Sindhu Verma

Neil Hendin

Bazhong Shen

Arzad Alam Kherani

C Ashok Kumar Reddy

Ying Xia

Phani Boddapati

Hariprasad Gangadharan

Vummintala Shashidhar

Joseph Saada

Ramasamy Palanisamy Pugazhanthi

Felix Varghese

Rakesh Agrawal

Yossi (Joseph) Saada

Shihhao Chiu

Chien Pin Chiu

Nicholas M McDonnell

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

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

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.

Adaptive phase-changing device power-saving operations

This document describes methods, devices, systems, and means for dynamic carrier subband operation for active coordination sets. A master base station selects a first carrier subband associated with a first Active Coordination Set (ACS) for joint communication with a user equipment (UE), coordinates the joint communication for the UE with other base stations in the first ACS, and monitors the joint communication with the UE. Based on the monitoring of the joint communication, the master base station selects a second carrier subband that is associated with a second ACS for the joint communication with the UE and coordinates with base stations associated with the second ACS to jointly communicate with the UE using the second carrier subband.

Dynamic carrier subband operation for active coordination sets

The subject matter described herein provides systems and techniques to automatically configure a proximal device, such as a smart watch, a tablet, or any other smart device, based on configuration information sent from the user equipment (UE) to the proximal device when the local communications connection, such as a wireless connection, between the devices is weak. If it is determined that there is a weak local communications connection between the UE and the proximal device the UE may automatically send network configuration information to the proximal device. If it is determined that there is a weak local communications connection between the UE and the proximal device the UE may automatically turn on its mobile hotspot, and automatically send the mobile hotspot configuration information to the proximal device.

Enhancement on smartwatch implementation

A base station (BS) supporting a protocol stack can implement a method for increasing network efficiency. The method includes transmitting (), to at least a first user equipment (UE), a group identity for a group of two or more UEs that includes the first UE. The method further includes generating (), by processing hardware of the base station and using the group identity, a data signal that includes aggregated downlink data. The aggregated downlink data includes (i) a UE-specific identity for at least two target UEs within the group and (ii) for each target UE of the at least two target UEs, a respective UE-specific identity, at an upper layer of the protocol stack, that indicates a location of the UE-specific downlink data intended for the target UE within the aggregated downlink data. The method further includes transmitting () the data signal to at least a portion of the group via a downlink data channel.

Virtual user equipment with data aggregation

Growing community of inventors

Sunnyvale, CA, United States of America

Erik Richard Stauffer