Abstract: A method of wireless communications by a user equipment (UE) includes communicating with a network based on a machine learning model for wireless communication. The method also includes monitoring a status of the machine learning model for wireless communication. The method further includes reporting the status of the machine learning model for wireless communication using a predetermined resource according to a predetermined format. The method also includes falling back to communicating with a fallback procedure, instead of the machine learning model for wireless communication, to maintain wireless communication with the network in response to the status of the machine learning model indicating a model failure.

Abstract: A method, a computer-readable medium, and an apparatus are provided for wireless communication at a network node having a split bearer configuration with a user equipment (UE). The network node measures, at a primary node (MN) or a secondary node (SN), a data burst statistic for the network node, the data burst statistic being based on at least one of a burst level throughput or a packet loss measurement for a data burst between the UE and the MN based on a first bearer in the split bearer configuration or between the UE and the SN based on a second bearer in the split bearer configuration. The network node provides the data burst statistic from the network node to at least one of the MN or a core network component.

Abstract: Methods, systems, and devices for wireless communications are described. Some wireless communications system may utilize an inter-system information report message (e.g., a self-organizing network (SON) information report message) to support inter-system mobility load balancing (MLB). For example, a first node, operating in accordance with a first radio access technology (RAT), may receive an information report message from a second node operating in accordance with a second RAT. The information report message may include a periodic load reporting request information element (IE) or an event-triggered load reporting request IE. In response, the first node may determine a traffic load based on the load reporting request and transmit, to the second node, an information report message which includes one or more IEs for reporting the determined traffic load. The exchange of the load information via the IEs may enable for MLB between nodes of different RAT.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, devices use machine learning (ML) models to support wireless communications. For example, a user equipment (UE) may download ML model information from a network to determine an ML model. The network may additionally configure a status reporting procedure, a fallback procedure, or both for the ML model. In some examples, based on a configuration, the UE may transmit a status report to a base station according to a reporting periodicity, a UE-based trigger, a network-based trigger, or some combination thereof. Additionally or alternatively, the UE may determine to fallback from operating using the ML model to operating in a second mode based on a fallback trigger. In some examples, to restore operating using a downloaded ML model, the UE may download an updated ML model or receive iterative updates to a previously downloaded ML model.

Abstract: 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.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first network entity may transmit a first message to a neighboring second network entity, where the first message indicates one or more of a discontinuous reception (DRX) timing of the first network entity or a discontinuous transmission (DTX) timing of the first network entity. The first network entity may start at least one of a DRX mode according to the DRX timing or a DTX mode according to the DTX timing. The second network entity may adjust communication scheduling or a measurement configuration. Numerous other aspects are described.

Abstract: Aspects described herein relate to improving layer 1 (L1)/layer 2 (L2) mobility in certain scenarios. In some aspects, an indication of mobility from a first cell in a candidate cell set configured for L1/L2 mobility to a second cell can be received, and one or more parameters related to mobility failure or mobility history or radio link failure (RLF) associated with the mobility to assist the network node in at least one of performing a subsequent L1/L2 mobility for the UE or avoiding RLF or mobility failure during the subsequent L1/L2 mobility can be transmitted. In other aspects, the indication of mobility can be transmitted and the one or more parameters can be received. In some aspects, a list of conditional primary secondary cell (PSCell) change (CPC) configurations can be communicated following a PCell switch.

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: A method of wireless communication includes determining, based on a plurality of network measurements performed by a user equipment (UE), one or more measurement log files associated with the plurality of network measurements. The method further includes receiving, by the UE from a network device, a request associated with the one or more measurement log files. The request indicates at least one measurement filter. The method further includes transmitting, by the UE to the network device, a response to the request. The response includes first measurement results of the one or more measurement log files selected based on the at least one measurement filter and excludes second measurement results of the one or more measurement log files based on the at least one measurement filter.

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: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may start a timer for obtaining quality of experience (QoE) measurements. The UE may obtain the QoE measurements until the timer expires. The UE may transmit information indicating the QoE measurements. Numerous other aspects are described.

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. 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: Certain aspects of the present disclosure provide techniques for wireless communications by a user equipment (UE). The UE receives a configuration for at least one machine learning function name (MLFN). The UE receives machine learning (ML) data associated with the at least one MLFN. The UE uses the ML data as an input for at least one of: operation or training of an ML model associated with the at least one MLFN.

Abstract: Methods and systems for compensating systematic errors across a fleet of metrology systems based on a trained error evaluation model to improve matching of measurement results across the fleet are described herein. In one aspect, the error evaluation model is a machine learning based model trained based on a set of composite measurement matching signals. Composite measurement matching signals are generated based on measurement signals generated by each target measurement system and corresponding model-based measurement signals associated with each target measurement system and reference measurement system. The training data set also includes an indication of whether each target system is operating within specification, an indication of the values of system model parameter of each target system, or both.

Abstract: A user equipment (UE) disconnects from next generation radio access network (NG-RAN) base station (BS) and receives, from the NG-RAN BS, a fallback indication. The UE then attempts to connect to an evolved-universal mobile telecommunications system terrestrial radio access network (E-UTRAN) BS. The UE determines that communication with the E-UTRAN BS was not established and identifies an alternate BS. After establishing communication with the alternate BS, the UE generates and transmits a report to the alternate BS including the fallback indication and information relating to the failed connection attempt with the E-UTRAN BS. The report is then conveyed to the NG-RAN BS for optimization of future fallback procedures.

Abstract: Methods, devices, and mechanisms for detecting and reporting successful secondary node and/or primary secondary cell group cell (PScell) changes are provided. In one example, a method of wireless communication performed by a first network unit comprises: transmitting, to a second network unit, an indication of a primary secondary cell group cell (PScell) change associated with a user equipment (UE); transmitting, based on the indication, a SPC configuration; and receiving a SPC report, wherein the SPC report is based on the SPC configuration and SPC information associated with the UE.

Abstract: Certain aspects of the present disclosure provide a method of wireless communication. An example method, performed at a first network entity, includes receiving a measurement report from a user equipment (UE) that supports dynamic mobility signaling via physical (PHY) layer or medium access control (MAC) layer signaling, wherein the measurement report includes at least one of PHY layer or MAC layer measurements, and transmitting the measurement report to a second network entity, based on an event trigger.

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: 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.