Abstract: 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.

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: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may determine one or more candidate base stations for a handover procedure for the UE. The UE may transmit an indication of the one or more candidate base stations to a serving base station. Numerous other aspects are provided.

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: A solar thermochemical processing system is disclosed. The system includes a first unit operation for receiving concentrated solar energy. Heat from the solar energy is used to drive the first unit operation. The first unit operation also receives a first set of reactants and produces a first set of products. A second unit operation receives the first set of products from the first unit operation and produces a second set of products. A third unit operation receives heat from the second unit operation to produce a portion of the first set of reactants.

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for a UE capability for AI/ML. A UE may receive a request from a network to report a UE capability for at least one of an AI procedure or an ML procedure. The UE may transmit to the network, based on the request to report the UE capability, an indication of one or more of an AI capability, an ML capability, a radio capability associated with the at least one of the AI procedure or the ML procedure, or a core network capability associated with the at least one of the AI procedure or the ML procedure.

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for an ML model training procedure. A network entity may receive a trigger to activate an ML model training procedure based on at least one of an indication from an ML model repository or a protocol of the network entity. The network entity may transmit an ML model training request to activate the ML model training at one or more nodes. The one or more nodes may be associated with a RAN that may transmit, based on receiving the ML model training request, ML model training results indicative of a trained ML model. In aspects, an apparatus, such as a UE, may train the ML model based on an ML model training configuration received from the RAN, and transmit an ML model training report indicative of the trained ML model.

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: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may determine one or more candidate base stations for a handover procedure for the UE. The UE may transmit an indication of the one or more candidate base stations to a serving base station. Numerous other aspects are provided.

Abstract: Methods, systems, and devices for wireless communications are described. A wireless device may receive an indication of a configuration for sensing measurement parameter reporting from a base station. The sensing measurement parameter reporting may be for a sensing operation. The wireless device may perform the sensing operation and transmit an indication of a measurement parameter value to the base station. The sensing measurement parameter value may be associated with a location of the wireless device. In some examples, the wireless device may transmit the indication of the sensing measurement parameter value based on performing the sensing operation.

Abstract: 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.

Abstract: The system includes a light source configured to emit light along an illumination path; a projection optical assembly disposed in the illumination path; a target disposed in the illumination path and configured to reflect the light along a collection path; a collection optical assembly disposed in the collection path; a detector disposed in the collection path and configured to detect the light reflected from the target and generate an output signal based on the detected light; and a processor in electronic communication with the detector and configured to generate a measurement of the target based on the output signal. The projection optical assembly defines a first numerical aperture at the target and the collection optical assembly defines a second numerical aperture at the target, and the first numerical aperture is slightly larger than the second numerical aperture for measurements of thick films and high aspect ratio structures.

Abstract: 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.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive, from a master node associated with a primary cell, a configuration for a conditional procedure for adding or changing a primary secondary cell (PSCell) associated with a secondary node. The UE may also receive, from the master node, a configuration for a handover procedure for the primary cell. The UE may transmit a report that includes information related to the conditional PSCell procedure, information related to the primary secondary cell, or both, based on a triggering order between the conditional PSCell procedure and the handover procedure.

Abstract: Disclosed are systems and techniques for wireless communications. For instance, a network entity can determine a first data heterogeneity level associated with input data for training a machine learning model. In some cases, the network entity can determine, based on the first data heterogeneity level, a first data aggregation period for training the machine learning model. In some aspects, the network entity may obtain a first set of updated model parameters from a first client device and a second set of updated model parameters from a second client device, wherein the first set of updated model parameters and the second set of updated model parameters are based on the first data aggregation period. In some examples, the network entity can combine the first set of updated model parameters and the second set of updated model parameters to yield a first combined set of updated model parameters.

Abstract: 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.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an apparatus of a central or management entity of an integrated access and backhaul (IAB) network may receive a report indicating one or more of a failure associated with a communication link associated with an IAB node included in the IAB network or a quality of service (QoS) associated with the communication link. The apparatus may modify a topography of the IAB network or routing within the IAB network when the report indicates the failure associated with the communication link. The apparatus may verify a QoS associated with the IAB network when the report indicates the QoS associated with the communication link. Numerous other aspects are described.

Abstract: Aspects of the present disclosure relate to wireless communications, and more particularly, to techniques for channel access for wireless communications, for example, in unlicensed frequency bands. In some cases, a UE may log information associated with channel access failures on one or more frequency bands in shared spectrum and provide the information to a network entity.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a base station, configuration information that indicates one or more first measurement types. Accordingly, the UE may transmit, to the base station, periodically and while the UE is in an idle mode or an inactive state, first measurements based at least in part on the one or more first measurement types indicated in the configuration information. In some aspects, the UE may further transmit, to the base station, second measurements based at least in part on a second measurement type. The first measurement type(s) may be associated with a minimization of drive test (MDT). Similarly, the second measurement type may be associated with an MDT. Numerous other aspects are provided.

Abstract: Aspects for sending structured measurement data based on a specific type of measurement requested by a network are disclosed. A user equipment (UE) may receive a request from a base station for minimization of driving test (MDT) data corresponding to a specified type of measurement by the UE from a larger plurality of available MDT measurements that the UE can make. The UE can send the requested data including a structure report having results for the specified measurement type to the base station. In other aspects, a base station may send a request to a UE for minimization of driving test (MDT) data corresponding to a type of measurement by the UE. The base station may receive the requested data corresponding to the type of measurement.