Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may report inference parameter sets supported by the UE to a network entity. The network entity may configure channel state information (CSI) reporting configurations for the UE, and may indicate a correspondence between the CSI reporting configurations and supported inference parameter sets. The UE may evaluate and report applicability information for the inference parameter sets and/or the CSI reporting configurations. For example, an inference parameter set may become non-applicable for CSI reporting at a UE based on changing conditions (e.g., channel or spatial conditions have changed as compared to the channel or spatial conditions used to train the relevant artificial intelligence or machine learning model by more than an allowable threshold for accuracy of the artificial intelligence or machine learning model).

Abstract: Methods, systems, and devices for wireless communications are described. The method may include a user equipment (UE) may receive a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. Further, the UE may receive one or more signals indicating input data for monitoring a performance of the machine learning model by the UE. Upon detecting the event trigger, the UE may transmit a report comprising the performance parameter.

Abstract: Methods, systems, and devices for wireless communications are described. A radio access network (RAN) node may receive a message including meta information for a machine learning model that is operated by one or more devices within the coverage area of the RAN node (e.g., supported by the RAN node), where the machine learning model may be transparent to the RAN node. The meta information may include applicability information for control and management of the machine learning model by the RAN node. In some examples, the RAN node may receive the meta information from various logical functions. In some other examples, the RAN node may receive the meta information from the one or more devices. In response to receiving the meta information, the RAN node may transmit a model management message instructing a device of the one or more devices to perform a model management operation for the machine learning model.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may initiate a quality of experience (QoE) session for a QE configuration at an application layer of the UE. The UE may provide, from the application to a radio resource control (RRC) layer of the UE, a session start or stop indication based at least in part on initiating the QoE session. The indication may include at least one of a service type, an RRC level identifier, a QoE reference, or a QoE measurement collection. The UE may provide, to a base station and based at least in part on the indication, information indicating the service type, the RRC level identifier, the QoE reference, or the QoE measurement collection. Numerous other aspects are described.

Abstract: The apparatus may be a wireless device configured to obtain, first information identifying a cell cluster comprising one or more cells of a plurality of cells, wherein the plurality of cells comprises a serving cell and one or more neighboring cells, and perform, based on the first information, a task associated with a ML model. The apparatus may be a network entity configured to transmit, for a UE, first information identifying a cell cluster comprising one or more cells of a plurality of cells, wherein the plurality of cells comprises a serving cell and one or more neighboring cells, and output, for a ML model associated with a task to be performed by the UE, a data collection configuration associated with the cell cluster.

Abstract: In accordance with some examples of the techniques described herein, a user equipment (UE) may be configured with probabilistic triggers for data collection, in which the UE may collect or buffer data when one of the triggers is active. Triggering rules or events may balance data collection or may ensure that data is not significantly biased by triggering events. The probabilistic trigger may help ensure diversity in artificial intelligence or machine learning (AI/ML)-based positioning or sensing data collection. In some approaches, a UE may indicate a capability to a network entity to collect or buffer data with probabilistic triggers. The UE may receive configuration information for collecting or buffering data with probabilistic triggers. In some aspects, the UE may request on-demand positioning reference signal (PRS) configurations (e.g., from a network entity, location server, or location management function (LMF)) for collecting or buffering data with probabilistic triggers.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive implicit or explicit information indicating whether to include location information in an uplink message based at least in part on whether the UE is a narrowband Internet of Things (NB-IoT) UE. The UE may transmit the uplink message in accordance with the implicit or explicit information. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communications are described. A network entity may obtain a first message indicating one or more artificial intelligence (AI)/machine learning (ML) capabilities of a user equipment (UE) and may further obtain, from an AI/ML service, a second message indicating one or more AI/ML service capabilities of the AI/ML service. The network entity may output a control message indicating one or more cross-node AI/ML configurations based on the one or more AI/ML capabilities of the UE and the one or more AI/ML service capabilities of the AI/ML service. The one or more cross-node AI/ML configurations may configure AI/ML functions of the UE for use with the AI/ML service. In some examples, life cycle management (LCM) procedures may be triggered by a UE, and the UE may transmit signaling associated with the LCM procedures (e.g., a request for LCM signaling or an indication of LCM).

Abstract: Methods, systems, and devices for wireless communication are described. A network entity may monitor a performance of a machine learning (ML) model or ML model-based functionality associated with a user equipment (UE). The UE may receive one or more control messages that indicate a life cycle management (LCM) operation for the ML model or ML model-based functionality. The one or more control messages may include an indication of whether the LCM operation is based on the performance of the MIL model or ML model-based functionality. In some examples, the indication may include or be an example of a performance report associated with the performance of the ML model or ML model-based functionality. The UE may perform the LCM operation for the ML model or ML model-based functionality. The UE or the network entity may transmit the indication to a server associated with the ML model or ML model-based functionality.

Abstract: Certain aspects of the present disclosure provide techniques for handling user equipment (UE) mobility for a cross-node artificial intelligence (AI) and/or machine learning (ML) session in a radio access network (RAN). A method of wireless communication by a first network entity includes obtaining, from a second network entity, an indication of a handover for a UE; obtaining, from a third network entity, an indication of first cross-node machine learning information associated with a cross-node machine learning session between the UE and the third network entity; providing, to the second network entity, an indication acknowledging the handover; and relaying communications between the UE and the third network entity for the cross-node machine learning session.

Abstract: Methods, systems, and devices for wireless communications are described. An access and mobility management function (AMF) may register a configuration of one or more RAN entities to a network repository function (NRF). The AMF may register information associated with a device with a UDM. A network data analytics function (NWDAF) may perform RAN discovery via the NRF, the UDM, or both. The NWDAF may receive an analytics request from a device, and the NWDAF may query the NRF, the UDM, or both for information about one or more targets of the analytics request, and the NWDAF may receive such information from the NRF, the UDM, or both. The NWDAF may transmit a data collection request to the RAN and the NWDAF may receive data back from the RAN.

Abstract: Certain aspects of the present disclosure provide techniques for handling user equipment (UE) mobility for a cross-node artificial intelligence (AI) and/or machine learning (ML) session in a radio access network (RAN). A method of wireless communication by a first network entity includes obtaining, from a second network entity, an indication of a handover for a UE; obtaining, from a third network entity, an indication of first cross-node machine learning information associated with a cross-node machine learning session between the UE and the third network entity; providing, to the second network entity, an indication acknowledging the handover; and relaying communications between the UE and the third network entity for the cross-node machine learning session.

Abstract: Certain aspects of the present disclosure provide techniques for managing cross-node artificial intelligence (AI) and/or machine learning (ML) operations in a radio access network (RAN). An example method of wireless communication by a first network entity includes obtaining machine learning input data associated with a user equipment (UE); providing, to a second network entity, an indication of machine learning output data generated using the machine learning input data; and providing, to the second network entity, control signaling for a cross-node machine learning session between the UE and the first network entity based at least in part on one or more performance indicators associated with the cross-node machine learning session.

Abstract: A carrier roller is mounted on a track frame and supports an upper strand of an endless linked chain, the carrier roller includes a generally cylindrical carrier roller body having an axis of rotation and a carrier sprocket extending radially relative to the axis of rotation so that the carrier sprocket is concentric with the generally cylindrical carrier roller body. The carrier sprocket includes a plurality of teeth separated by concave valleys configured to receive the cylindrical bushings of the endless linked chain in the concave valleys between the teeth, the concave valleys between the teeth being radially offset from the generally cylindrical carrier roller body such that a weight of the upper strand of the endless linked chain is primarily carried by the engagement of the concave valleys with the cylindrical bushings of the endless linked chain.

Abstract: A method of wireless communication by a base station includes receiving a user equipment (UE) radio capability and a UE machine learning capability. The method also includes determining a neural network function (NNF) based on the UE radio capability. The method includes determining a neural network model. The neural network model includes a model structure and a parameter set, based on the NNF, the UE machine learning capability, and a capability of a network entity. The method also includes configuring the network entity with the neural network model.

Abstract: Methods, systems, and devices for wireless communication are described. A user equipment (UE) may transmit, to a network entity, a consent profile registration request, the consent profile registration request identifying a user identifier. The UE may transmit, to the network entity, a consent profile based at least in part on the consent profile registration request, the consent profile comprising one or more consent types for the user identifier that configure one or more consent parameters for the user identifier on a least a per-user basis.

Abstract: Embodiments provide methods and systems of enabling an electronic payment from a merchant device using customer credential when a customer does not have mobile device or card with him. The customer enters customer credential, associated with a customer wallet, on a merchant wallet application accessible in the merchant device. The customer credential and merchant credential are transmitted to a server system associated with a payment network. The server system validates the customer credential, generates and transmits a pull payment request to a customer wallet server for approval of the payment request for transferring funds from customer wallet to the merchant wallet. After the approval, the server system transmits the successful payment response to the merchant device.

Abstract: Example implementations include a method, apparatus and computer-readable medium of wireless communication by a user equipment (UE), comprising receiving parameter set configuration information from a network entity, the parameter set configuration information corresponding to a model structure employed in a machine learning operation by the UE for the wireless communication. The implementations further include activating a parameter set in response to an activation condition, the parameter set identified within the parameter set configuration information as being associated with the activation condition.

Abstract: Certain aspects of the present disclosure provide techniques for managing cross-node artificial intelligence (AI) and/or machine learning (ML) operations in a radio access network (RAN). An example method of wireless communication by a first network entity includes obtaining machine learning input data associated with a user equipment (UE); providing, to a second network entity, an indication of machine learning output data generated using the machine learning input data; and providing, to the second network entity, control signaling for a cross-node machine learning session between the UE and the first network entity based at least in part on one or more performance indicators associated with the cross-node machine learning session.

Abstract: Methods, systems, and devices for wireless communications are described. In some implementations, a network exposure service may obtain a first request message requesting information associated with one or more radio access network (RAN) configuration states of a RAN. In some examples, a network exposure service may output, to at least a second device based at least in part on obtaining the first request message, a first response message indicating that the information associated with the one or more RAN configuration states of the RAN is supported. In such examples, the network exposure service may output based at least in part on outputting the first response message, a notification message or indication comprising a report of the one or more RAN configuration states.