Abstract: A monolithic optical retarder formed from a monolithic prism may include an input face for receiving a light beam, an output face aligned with an optical axis of the light beam prior to entering the input face, and three or more reflection faces. The three or more reflection faces may be oriented to provide an optical path for the light beam from the input face to the output face via reflection by the three or more reflection faces, where the monolithic optical retarder imparts a selected optical retardation on the light beam based on total internal reflection on at least one of the reflection faces. Further, the input face, the output face, and the three or more reflection faces may be oriented such that an optical axis of the light beam exiting the output face is equal to the optical axis of the light beam entering the input face.

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: 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: Methods and systems for performing optical measurements of semiconductor structures while modulating both an electric field within one or more structures under measurement and the measurement light employed to measure the one or more structures are presented herein. Spectroscopic ellipsometry, spectroscopic reflectometry, and angle resolved spectroscopic reflectometry measurements are enhanced by modulation of the electric field of the structures under measurement. The modulation of the electric field changes the dielectric function of the materials under measurement. Measurements are performed with an enriched data set including measurement signals collected from one or more structures under time varying optical and electric field conditions. This reduces parameter correlation among floating measurement parameters and improves measurement accuracy.

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: 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.

Abstract: 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.

Abstract: Aspects present herein relate to methods and devices for wireless communication including an apparatus, e.g., a UE and/or a base station. The apparatus may receive, from a base station, a logged measurement configuration including a PLMN ID and a NID, the logged measurement configuration further including at least one of a trace reference, a logging area, a MDT PLMN list, or a MDT NPN list. The apparatus may also store the PLMN ID and the NID based on the received logged measurement configuration. Additionally, the apparatus may compare the PLMN ID and the NID to an MDT SNPN list to identify if the PLMN ID and the NID are included in the MDT SNPN list. The apparatus may also transmit, to the base station, an availability indicator if the PLMN ID and the NID are included in the MDT SNPN list.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may communicate with a network entity within a wireless communications network. The UE may transmit a request for information to the network entity and, in response to the request, the UE may receive the requested information from the network entity. For example, the UE may request data from one or more data repositories associated with the network entity. In some examples, the information request may be associated with one or more measurements associated with operations of the network. In some instances, the UE may use a machine learning model to perform training or inference based on the information associated with the one or more measurements.

Abstract: 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.

Abstract: 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.

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: 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: 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.

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: Methods, systems, and devices for wireless communications are described. In some systems, a first base station and a second base station may be neighbor base stations and the first base station may receive, from the second base station, information relating to a multicast-broadcast services (MBS) session context of an MBS session supported by the second base station. The first base station may use the received MBS session context information to control communication or connections between the first base station and one or more user equipment (UEs) that are served by the first base station. For example, the first base station may use the received MBS session context information for target cell selection in a handover procedure, an MBS-specific measurement configuration, interference avoidance, or system information block (SIB) construction, among various other use cases.

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.