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.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first network node in a radio access network (RAN) may transmit mobility history data for a user equipment (UE) to a second network node in a core network associated with the RAN. The first network node may receive a UE mobility prediction model that is based at least in part on the mobility history data from the second network node. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit an update to a previously reported set of UE artificial intelligence (AI) or machine learning (ML) capabilities. The UE may receive a configuration associated with performance of UE AI or ML operations based at least in part on the update. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communications are described for capability indications by a reduced capability user equipment (UE) and UE-based performance measurement parameter reporting or self-organizing network (SON) parameter reporting based on the capability indications. A reduced capability UE may transmit a set of capabilities to a base station that indicate various types of parameter reporting that are supported at the UE. The base station may identify the UE as a reduced capability device, and set one or more reporting parameters based on the indicated UE capabilities. The base station may provide a reporting configuration to the UE with the one or more reporting parameters, and the UE may provide reporting of measured parameters (e.g., performance measurement parameter reporting or SON parameter reporting) based on the reporting configuration.

Abstract: Methods, systems, and devices for wireless communications are described. A communication link between the UE and a serving base station may be established. The UE may measure, at an application layer of the UE, a set of quality of experience variables associated with different service types. The UE may measure, at an access stratum of the UE, a set of radio resource management variables associated with the communication link between the UE and the serving base station and a communication link between the UE and a corresponding set of one or more neighboring base stations associated with the communication links. The UE may transmit a measurement report to the serving base station indicating information associated with the quality of experience variables and the radio resource management variables in a multi-layer readable format.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a control message that includes a list of associated identifiers (IDs), and the control message may further indicate whether each associated ID included in the list is a current associated ID or a future associated ID (e.g., whether a respective associated ID represents a current or future network-side additional condition). In some aspects, the information indicating whether an associated ID is a current of future associated ID may be conveyed to the UE explicitly or implicitly. In some examples, the UE may receive an indication of a priority across associated IDs included in the list. Additionally, or alternatively, the UE may receive an indication of a priority across inference configurations for one or more current associated IDs. The priority may be configured across associated IDs and used to determine which associated ID(s) to prioritize.

Abstract: Methods, systems, and devices for wireless communications are described. The described techniques may enable a network entity to update a training data collection configuration for performing, logging, or reporting measurements of a set of training resources. For example, a user equipment (UE) may perform and log measurements of the set of training resources according to the training data collection configuration. The UE may indicate the logged measurements to the network entity, and the network entity may update the training data collection configuration, or may pause or initiate training data collection. Additionally, or alternatively, the UE may indicate a request for the network entity to update the training data collection configuration or to pause or initiate training data collection. The network entity may accordingly update the training data collection configuration.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a request to report a set of key performance indicators (KPIs) associated with one or more artificial intelligence (AI) functions operated by the UE. In response to receiving the request, the UE may determine the KPIs. In some examples, in response to determining the KPIs, the UE may transmit a report including an indication of the KPIs to one or more network functions, where the one or more network functions may utilize such KPIs to adjust one or more parameters at the UE, adjust the AI functions operated at the UE, or both. Accordingly, the UE may receive management information from the one or more network functions indicating the adjustment to the one or more parameters at the UE, the adjustment to the AI functions operated by the UE, or both.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a message from a network entity indicating a set of machine learning models, a set of parameter sets, or both and one or more usage conditions associated with the machine learning models and parameter sets. Based on a usage condition being satisfied, the UE may select a machine learning model, a parameter set, or both for generating a machine learning inference. For example, the UE may select the machine learning model or the parameter set based on a priority, whether sufficient input data is provided, or based on other usage conditions. The UE may generate the machine learning inference using the selected machine learning model or the selected parameter set, and the UE may transmit a report indicating an output of the machine learning inference to the network entity.

Abstract: Methods, systems, and devices for wireless communications are described that provide for machine learning model generalization in which a machine learning model may be initially configured for a first set of conditions, and the machine learning model may be generalized to apply to one or more conditions that are outside of the first set of conditions. A network entity may provide a user equipment (UE) with one or more machine learning models, the first set of conditions, and information for model evaluation in which one or more key performance indicators (KPIs) may be evaluated for conditions outside of the first set of conditions. The UE may measure the KPIs, and transmit an evaluation report to the network entity that indicates the KPIs for the identified condition. The network entity may generalize the corresponding model based on the reported KPIs, and provide an updated machine learning model.

Abstract: Apparatus, methods, and computer program products for wireless communication are provided. An example method may include detecting, at a session management and orchestration (SMO), a trigger for conflict management associated with at least one application. The example method may further include providing, at the SMO based on the trigger for the conflict management, a conflict evaluation or a conflict mitigation.

Abstract: Some examples of the techniques described herein may include handling training data collection or reporting of artificial intelligence or machine learning (AI/ML)-based positioning based on an access stratum state. Training data collection or reporting of AI/ML-based positioning may be performed with one or more AI/ML-related use cases. In some approaches, a user equipment (UE) may be configured for training data collection for one or more use cases (e.g., AI/ML-related use cases for one or more access stratum states), where training data collection may occur in one or more access stratum states. Collected training data may be reported to different network entities. In some examples, a UE may store logged training data at the access stratum layer. In some examples, an indication of availability may be communicated. For instance, one or more indications may be utilized regarding access stratum states in which training data collection may be performed.

Abstract: Some examples of the techniques described herein may include handling one or more data collection procedures based on an access stratum state or access stratum buffer status. In some approaches, a user equipment (UE) may be configured for training data collection for one or more use cases (e.g., artificial intelligence or machine learning (AI/ML)-related use cases for one or more access stratum states). Collected training data may be reported to different network entities (e.g., to a network node for channel state information (CSI) feedback, beam management, or AI/ML based mobility, or to a location management function (LMF) for positioning, among other examples). In some approaches, data collection configuration may be performed based on an access stratum state. For instance, one or more indications may be utilized regarding one or more access stratum states in which training data collection may be performed.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a control message that indicates one or more reporting parameters for the UE and includes an indication of whether the one or more reporting parameters are applicable to collecting data for training a machine learning model. The machine learning model may be associated with event prediction at a network entity. The UE may perform, based on an indication that one or more reporting parameters are applicable to collecting for training a machine learning model, a measurement procedure to obtain one or more measurements in response to detection of the one or more events. The UE may transmit a report that includes the one or more measurements in accordance with the one or more reporting parameters.

Abstract: Apparatus, methods, and computer program products for wireless communication are provided. An example method may include receiving, from a first network node, a first training data collection configuration and a first identifier (ID) associated with the first training data collection configuration and the first network node, where the first ID is indicative of a first vendor associated with the first network node. The example method may further include obtaining a set of training data based on the first training data collection configuration and the first ID. The example method may further include receiving, based on a handover from the first network node to a second network node, a second ID associated with the second network node, where the second ID is indicative of a second vendor associated with the second network node.

Abstract: Methods, systems, and devices for wireless communication are described. A user equipment (UE) may receive a first control message indicating a set of mobility parameters associated with a lower layer handover procedure, which may be a layer 1 (L1)/layer 2 (L2) handover procedure. The UE may receive a second control message indicating a first or second reset procedure, which may each indicate a respective set of operating parameters for a higher layer than L1/L2 in the protocol stack. The UE may perform the lower layover handover procedure, which may include switching from a first serving cell to a second serving cell. The UE may perform the first or second reset procedure based on the switching, where the first reset procedure may include maintaining the operating parameters of the second serving cell, and the second reset procedure may include switching the operating parameters of the second serving cell.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a first network node, a training data collection configuration that indicates prioritization information, wherein a training data override protection is based at least in part on the prioritization information. The UE may collect training data based at least in part on the training data collection configuration and the prioritization information. The UE may transmit, to the first network node, the training data. Numerous other aspects are described.