Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a base station (BS) may receive a first downlink determining, based at least in part on an uplink transmission schedule for a user equipment (UE), one or more communications that are to be transmitted as a cross link interference reference signal (CLI-RS) during a time window. The BS may transmit, to the UE, an instruction to transmit the one or more communications as the CLI-RS during the time window. Numerous other aspects are provided.

Abstract: Certain aspects of the present disclosure provide techniques for configuring machine learning models on user equipment, including reduced capability user equipment.

Abstract: Aspects described herein relate to receiving a configuration for cross-link interference (CLI) measurement reporting, wherein the configuration includes, for each component carrier of one or more component carriers, one or more parameters for measuring signals on the component carrier, and a resource setting indicating resources of the component carrier over which to measure signals. One or more measurements of signals received on the resources indicated by the resource setting for the component carrier can be measured based at least in part on the one or more parameters, and reported to a base station.

Abstract: Methods, systems, and devices for wireless communication at a user equipment (UE) are described. A UE may receive control signaling from a serving base station. The control signaling may indicate a resource pattern to monitor for interference caused by downlink transmissions from a second base station. The UE may then perform a received signal strength indicator measurement of the downlink transmissions from the second base station in accordance with the resource pattern. Upon performing the received signal strength indicator measurement, the UE may transmit a measurement report including an indication of the received signal strength indicator measurement.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a base station may determine that a user equipment (UE) is to use a differential reporting mode based at least in part on a channel state information (CSI) report from the UE. The base station may transmit to the UE, signaling to cause the UE to use the differential reporting mode based at least in part on determining that the UE is to use the differential reporting mode. Numerous other aspects are provided.

Abstract: Aspects of the present disclosure relate to wireless communication. In aspects, a base station may determine one or more signaling parameters to be used to transmit one or more reference signals that identify the base station in association with remote interference management, wherein the one or more signaling parameters include at least one of: multiple time periods in which the one or more reference signals are to be transmitted; one or more mini-bands, within one or more sub-bands, in which the one or more reference signals are to be transmitted; one or more scrambling codes, selected from a pool of scrambling codes, to be used to scramble the one or more reference signals; or a combination thereof. The base station may transmit the one or more reference signals using the one or more signaling parameters to identify the base station in association with remote interference management. Other aspects are provided.

Abstract: A method of wireless communication at a user equipment (UE) is provided. The method includes receiving, from a scheduling entity, an indication of at least two different cross link interference (CLI) measurement resources of a message for transmission from a transmission source. The method also includes receiving, from an intelligent reflecting surface (IRS), a reflected transmission of the message originating from the transmission source, wherein the reflected transmission of the message is reflected by the IRS based on one or more IRS coefficients. The method further includes determining one or more CLI measurement parameters to generate a report. The method includes transmitting, to the scheduling entity, a CLI measurement report. The method also includes receiving, from the IRS, another reflected transmission of the message originating from the transmission source using one or more configured IRS coefficients that are configured based on the CLI measurement report.

Abstract: Certain aspects of the present disclosure provide techniques for remote interference mitigation between base stations using reference signals. Certain aspects provide a method for wireless communication by a first base station (BS). The method includes receiving a reference signal (RS) from a second BS. The method further includes performing interference measurement corresponding to interference from the second BS based on the RS being associated with the second BS.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may indicate a support for an end-to-end multi-block machine learning application, a first UE capability for a backbone block of the multi-block machine learning application that makes up one or more front-end layers (e.g., one or more backbone layers), a second UE capability for a task-specific block of the multi-block machine learning application that makes up the end layer (s) of the end-to-end model (e.g., one or more task-specific layers), or a combination thereof. In some examples, the UE may transmit separate indications for the first UE capability and for the second UE capability. Additionally or alternatively, the UE may transmit a general machine learning capability indication, where a base station then determines the first UE capability for the base stage and the second UE capability for the task-specific stage from the general machine learning capability.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a node may determine a first set of time-frequency resources, within a time duration, for full-duplex communications and a second set of time-frequency resources, within the time duration, for non-full-duplex communications. The node may transmit to a wireless communication device and based at least in part on determining the first set of time-frequency resources and the second set of time-frequency resources, an indication of the first set of time-frequency resources, the second set of time-frequency resources, and corresponding transport formats for the first set of time-frequency resources and the second set of time-frequency resources. Numerous other aspects are provided.

Abstract: Example aspects include a method, apparatus, and computer-readable medium of managing cross-link interference (CLI) by a first network node of a wireless communication network, comprising generating a reflection coefficient measurement configuration for an intelligent reflecting surface (IRS) controlled by the first network node to mitigate the CLI caused by a first user equipment (UE) controlled by the first network node to a second UE controlled by a second network node. The aspects further include transmitting network information comprising the reflection coefficient measurement configuration. Additionally, the aspects further include receiving, in response to transmitting the network information, CLI measurement information associated with the second UE. Additionally, the aspects further include identifying a particular reflection coefficient from a set of different reflection coefficients associated with the reflection coefficient measurement configuration.

Abstract: A first user equipment (UE) receives, from a scheduling entity, one or more indications of one or more parameters associated with an uplink (UL) transmission from a second UE. The UL transmission includes one or more cross link interference (CLI) measurement resources. The first UE configures one or more dedicated tracking loops for receiving the one or more CLI measurement resources of the UL transmission based on the one or more indications of the one or more parameters. The first UE receives, from the second UE, the one or more CLI measurement resources of the UL transmission using the one or more dedicated tracking loops according to the one or more parameters, while also receiving a downlink (DL) transmission from the scheduling entity. The first UE determines one or more CLI measurements using the one or more CLI measurement resources of the UL transmission.

Abstract: Embodiments described herein provide for an RIS-aided determination of the location of mobile device that enables the signal reflected by the RIS to be identified through channel estimation. More specifically, an Orthogonal Cover Code (OCC)-based approach may be taken where the RIS is configured to use different weights to reflect different reference signals, enabling a receiving device to identify the reflected signal. This can be utilized on signals used for positioning of the mobile device. Additionally or alternatively, this can be used for positioning of the RIS.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a control message identifying a backbone model that is combinable with at least one task-specific model to generate a multi-model machine learning application. The UE may receive the backbone model and a first task-specific model identified by the control message. The UE use a multi-model machine learning application that is a combination of the backbone model and the first task-specific model to process one or more received signals to generate one or more outputs. The UE may communicate with a wireless device based on the one or more outputs.

Abstract: Methods, systems, and devices for wireless communications are described. A first user equipment (UE) may transmit, to a base station, capability signaling including an indication of a capability of the first UE to prioritize reception of downlink messages scheduled via a first type of scheduling which at least partially overlap with resources used to perform cross-link interference (CLI) measurements. The first UE may receive control signaling indicating a first set of resources for a downlink message, and additional control signaling indicating a second set of resources usable by the first UE to perform CLI measurements on signals transmitted by a second UE, where the second set of resources at least partially overlap with the first set of resources in a time domain. The first UE may then receive the downlink message from the base station within the first set of resources based on the indication of the capability.

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: 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: Disclosed are techniques for wireless positioning. In an aspect, a first network node receives, from a second network node, a positioning reference signal having a first reception time at the first network node, receives, from a first user equipment (UE), a first uplink positioning reference signal having a second reception time at the first network node, receives, from a second UE, a second uplink positioning reference signal having a third reception time at the first network node, and enables a first reference signal time difference (RSTD) measurement to be calculated for the first UE and a second RSTD measurement to be calculated for the second UE based on the first reception time, the second reception time, the third reception time, and other measurements.

Abstract: A configuration for reporting OOD samples for neural network optimization. The apparatus receives, from a base station, a configuration to report an OOD dataset for a machine learning model. The apparatus detects an occurrence of one or more OOD events. The apparatus reports the OOD dataset comprising the one or more OOD events based on the configuration to report OOD dataset. The apparatus receives, from the base station, an update to the machine learning model. The OOD dataset may comprise raw data related to the one or more OOD events, or may comprise extracted latent data corresponding to features of raw data related to the one or more OOD events.

Abstract: Techniques to incorporate quality of experience (QoE) in mobility management are disclosed. A user equipment (UE) may provide a QoE report to network (e.g., cell) upon detection of a QoE report triggering event (e.g., mobility management, environmental trigger, etc.). The QoE report may comprise information related to QoE metrics such as uplink (UL) latency, UL throughput, UL error rate, downlink (DL) frequency, DL throughput, DL error rate, handover (HO) frequency, beam switch frequency, UE power consumption, etc. The network may take actions such as sending a mobility management command if it is decided that QoE should be restored to the UE.