Qualcomm Incorporated

Shankar Krishnan

Xipeng Zhu

Rajeev Kumar

Gavin Bernard Horn

Ozcan Ozturk

Aziz Gholmieh

Taesang Yoo

Eren Balevi

Punyaslok Purkayastha

Rajat Prakash

Umesh Phuyal

Luis Fernando Brisson Lopes

Alberto Rico Alvarino

Bharat Shrestha

Linhai He

Prasad Reddy Kadiri

Vanitha Aravamudhan Kumar

Vishal Dalmiya

Xiaoxia Zhang

Tingfang Ji

Karl Georg Hampel

Masato Kitazoe

Sitaramanjaneyulu Kanamarlapudi

Karthika Paladugu

Mutaz Zuhier Afif Shukair

Aziz Gholmich

Methods, systems, and devices for wireless communications supporting network coordination during path switching and multi-path scenarios are described. For example, a first network entity may receive, from a first user equipment (UE), a measurement report indicating a degradation of communications between the first UE and the first network entity. The first network entity may transmit, to a second network entity, a request message to switch a connection between the first UE and the first network entity to a connection between the first UE and the second network entity. The second network entity may transmit, to the first network entity, an acknowledgment based on the request. The first network entity may configure the first UE for the new connection and the second network entity may configure the second UE for the new connection. The first UE may communicate with the second network entity via the second UE.

Network coordination during path switching and multi-path scenarios

Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning. The UE may transmit, to a base station, a request message that includes an indication of a machine learning model or a neural network function based at least in part on a trigger event. In response to the request message, the base station may transmit a machine learning model, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function and may transmit an activation message to the UE to implement the machine learning model and the neural network function.

Configuring a user equipment for machine learning

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may measure quality of experience (QoE) metrics and transmit a QoE report that is formatted to be readable by a base station, such that a base station may receive the QoE report and independently perform adjustments associated with the service being utilized by the UE. A UE may receive a configuration message that includes a first configuration for reporting QoE measurements to a base station and a second configuration for reporting QoE measurements to a QoE server. The UE may measure one or more QoE metrics in accordance with the configuration message, and generate a first report for the base station based on the QoE measurements and the first configuration. Upon generating the report, the UE may transmit the first report to the base station in accordance with the first configuration.

Quality of experience measurement and reporting

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a primary cell or a primary secondary cell (PSCell), configuration information for a conditional PSCell addition or change (CPAC), the configuration information indicating configurations for one or more candidate PSCells. The UE may detect a failure, associated with the conditional PSCell addition or change, associated with a first target PSCell from the one or more candidate PSCells. The UE may perform an action to recover the CPAC based at least in part on detecting the failure. Numerous other aspects are described.

Conditional primary secondary cell addition or change recovery procedure

Certain aspects of the present disclosure provide techniques for data collection for non-terrestrial networks (NTN). One aspect provides a method for wireless communications by a user equipment (UE). The method generally includes transmitting an indication of a capability of the UE to connect to a network via both terrestrial network (TN) cells and non-terrestrial network (NTN) cells and transmitting one or more data collection reports in accordance with the indicated capability.

Data collection reporting for non-terrestrial network cells

Methods, systems, and devices for wireless communications are described. In some examples, a user equipment (UE) may support dual connectivity. That is, the UE may establish communications with a master node and a secondary node of a wireless communications network. The master node may correspond to a master cell group (MCG) and the secondary node may correspond to a secondary cell group (SCG). In some examples, as described herein, the UE operating in dual connectivity may collect data for optimization of the wireless communications network or upon detecting a failure associated with the master cell group or the SCG and transmit the collected data to a network entity (e.g., one of the master node or the secondary node), where the collected data is based on the SCG being in a deactivated state. In some examples, upon receiving the collected data, the network entity may attempt to recover from the failure.

Data collection enhancements for secondary cell groups

This disclosure provides systems, methods, and devices for wireless communication that support AI model-based enhancements for RRC IDLE and INACTIVE state operations. In a first aspect, a method of wireless communication includes receiving, by a wireless communication device, artificial intelligence (AI) model configuration information for IDLE/INACTIVE state procedures; retrieving, by the wireless communication device, an AI model for IDLE/INACTIVE state procedures based on the AI model configuration information; and performing, by the wireless communication device, one or more IDLE/INACTIVE state procedures based on the AI model. Other aspects and features are also claimed and described.

Artificial intelligence based enhancements for idle and inactive state operations

A source network node and a UE may obtain at least one mobility related prediction associated with the UE or at least one target network node, the at least one mobility related prediction being derived by at least one neural network, and the source network node may handover the UE from the source network node to the at least one target network node based on the at least one mobility related prediction. The target network node may receive the handover request, obtain at least one mobility related prediction associated with the UE or the target network node, and output for transmission a handover request ACK, the handover request ACK based at least in part on the at least one mobility related prediction.

AI/ML based mobility related prediction for handover

Methods, systems, and devices for wireless communications are described, in which a base station may determine a load balancing adjustment for traffic communicated through the base station. The load balancing adjustment may be provided at a beam level for one or more beams of a plurality of beams, at a network slice level for one or more network slice IDs of a number of network slice IDs, or combinations thereof. The base station may use the load balancing adjustment to identify one or more updated parameters for triggering a handover of at least one user equipment (UE) between beams or slice IDs of a same cell or different cells. The base station may format a mobility change request that indicates the one or more parameters that are updated, and transmit the mobility change request to another base station to establish parameters for handovers between the base stations.

Enhancements to mobility settings change procedures

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit capability information that indicates support for one or more model combinations of machine learning (ML) models, wherein the capability information further indicates one or more performance parameters of an ML model of the ML models with respect to a model combination of the one or more model combinations that includes the ML model. The UE may receive one or more indications to use one or more of the ML models based at least in part on the capability information. Numerous other aspects are described.

Performance indicators for combinations of machine learning models

Methods, systems, and devices for wireless communication are described. A user equipment (UE) may receive control signaling indicating a measurement configuration for minimization of drive test measurement and reporting supporting broadcast receptions within a single-frequency network. The UE may determine, based at least in part on the measurement configuration, a set of measurements for the single-frequency network. The UE may transmit a minimization of drive test report indicating the set of measurements.

Data collection enhancements for multicast broadcast services

Aspects relate to measurement and event reporting from a distributed unit (DU) of a disaggregated base station to a central unit (CU) of the disaggregated base station. The CU can configure the DU with a measurement configuration associated with at least one value to be obtained by the DU and a reporting configuration for reporting the at least one value to the CU. The measurement reports can be sent by DU periodically or the measurement reports can be event-triggered based on the reporting configuration. In addition, the measurement reports can be UE-specific or DU/cell-specific. The measurement reports may include random access channel (RACH) reports, uplink measurement reports, radio link protocol (RLC) reports, medium access control (MAC) protocol reports, and other types of measurement or event-based reports.

Distributed unit (DU) measurement and event reporting in disaggregated base station

Certain aspects of the present disclosure provide techniques and apparatus for determining neural network functions (NNFs) and configuring and using corresponding machine learning (ML) models for performing one or more ML-based wireless communications management procedures. An example method performed by a user equipment includes transmitting, to a base station (BS), UE capability information indicating at least one radio capability of the UE and at least one machine learning (ML) capability of the UE and receiving, from the BS based on the UE capability information, ML configuration information indicating at least one neural network function (NNF) and at least one ML model corresponding to the at least one NNF.

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San Diego, CA, United States of America

Shankar Krishnan