Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive an indication of an interference prediction configuration. The UE may transmit an interference prediction report based at least in part on the interference prediction configuration. Numerous other aspects are described.

Abstract: Certain aspects of the present disclosure provide techniques for wireless communications by a user equipment (UE), including receiving, from a network entity, an indication to report at least one of a channel quality indicator (CQI) or a pre-coding matrix indicator (PMI) for one or more future communications resources and sending, to the network entity, a channel state feedback (CSF) report indicating at least one of a predicted CQI or a predicted PMI for the one or more future communications resources prior to the one or more future communications resources occurring in time.

Abstract: Apparatus, methods, and computer-readable media for facilitating reference signal multiplexing with downlink data are disclosed herein. An example method for wireless communication at a user equipment (UE) includes receiving, from a base station, a reference signal configuration associated with at least one of a tracking reference signal (TRS) and a channel state information reference signal (CSI-RS), the reference signal configuration comprising a symbol level rate matching configuration, a precoder configuration, or a guard tone configuration. The example method also includes receiving, from the base station, a transmission comprising at least a reference signal and data based on the reference signal configuration.

Abstract: Certain aspects of the present disclosure provide techniques for wireless communications by a user equipment (UE), including receiving, from a network entity, one or more parameters for performing interference prediction at the UE; predicting, for each of at least one communications resource, a predicted interference at the UE, wherein the predicted interference comprises a plurality of probability values, each probability value of the plurality of probability values being associated with a different class of a plurality of classes, each class of the plurality of classes associated with a corresponding range of interference power; and sending, to the network entity, a report based on the predicted interference.

Abstract: A first UE may identify, in at least one round of a federated learning procedure, at least one gradient update based on local data and a local copy of a machine learning model associated with the federated learning procedure. The first UE may transmit, based on a non-coherent OTA aggregation scheme and for a network node, an indication of the at least one gradient update via a set of resources associated with the non-coherent OTA aggregation scheme. The indication of the at least one gradient update may include at least one sequence. A transmit power associated with the at least one sequence may be based at least in part on a magnitude of the at least one gradient update. Based on the non-coherent OTA aggregation scheme, the network node may obtain the combined gradients associated with all participating UEs by averaging the received power over the set of resources.

Abstract: Aspects described herein relate to receiving control information related to scheduling multiple resources for wireless communications, including one of semi-persistent scheduling (SPS) for multiple downlink resources for receiving downlink communications or configured grant (CG) scheduling for multiple uplink resources for transmitting uplink communications, and transmitting, for a resource of the multiple resources, in an uplink resource, and based on receiving the control information, a predicted channel report indicating predicted channel measurements for the resource before receiving a downlink communication or transmitting an uplink communication over the resource. Other aspects relate to transmitting the control information and receiving the predicted channel report.

Abstract: Methods, systems, and devices for wireless communications at a user equipment (UE) are described. The UE may receive a control message indicating a set of multiple resources for which the UE may predict interference information. The UE may select a codebook from a set of multiple codebooks to compress the interference information for the set of multiple resources that follows the control message. The UE may select the codebook based on the set of multiple resources associated with the interference information The UE may transmit a report indicating the interference information predictions to a network entity. That is, the report may include the interference information that may be compressed using the selected codebook. The network entity may receive the report and decompress the interference information to retrieve the predicted interference information for a wireless communications system.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a configuration associated with an interference report. The UE may transmit the interference report in accordance with the configuration, wherein the interference report includes an indication of one or more parameters of a Gaussian mixture distribution that is fitted to an interference information distribution. Numerous other aspects are described.

Abstract: A UE may identify, in at least one round of a federated learning procedure, at least one gradient update based on local data and a local copy of a machine learning model associated with the federated learning procedure. The UE may transmit, in the at least one round of the federated learning procedure, for a network node, an indication of the at least one gradient update based on a sign of the at least one gradient update and a non-coherent orthogonal modulation scheme. The network node may identify, in at least one round of the federated learning procedure, an accumulated sign of the at least one gradient update based on a first received power associated with the at least one first resource and a second received power associated with the at least one second resource.

Abstract: Aspects of the disclosure are directed to quantized orthogonal modulation for non-coherent over-the-air (OTA) federated learning (FL). In some examples, a wireless device may compare a gradient to a threshold value to determine whether the gradient satisfies a threshold condition. The wireless device may also output, for transmission to a network node, signaling via one of a first resource or a second resource, wherein the signaling is outputted via the first resource if the gradient satisfies the threshold condition, and wherein the signaling is outputted via the second resource if the gradient does not satisfy the threshold condition.

Abstract: Certain aspects of the present disclosure provide techniques for receiving an indication of variable tracking reference signal (TRS). A method that may be performed by a user equipment (UE) includes determining a configuration indicating a value for a variable tracking reference signal (TRS) density, monitoring for TRSs from a base station (BS) according to the indicated value for the variable TRS density, and performing at least one of frequency tracking or timing tracking based on the monitoring.

Abstract: Disclosed is a method for wireless communication. The method comprises receiving, from a network node, a reference signal (RS) of a first type. An input signal of a second type is determined based on the reference signal. Signals of the second type are associated with training a first machine learning (ML) model applied by a UE to estimate a parameter used in wireless communications by the UE. The input signal of the second type is applied to the first ML model to estimate the parameter used in wireless communications by the UE.

Abstract: A method for wireless communication at a first user equipment (UE) includes transmitting, to a network node, a first message indicating one or more distributions of a group of local data instances associated with a machine learning model at the first UE, each local data instance of the group of local data instances associated with a class of a group of classes. The method also includes receiving, associated with transmitting the first message, from the network node, a second message indicating an update to the group of local data instances, for satisfying one or more data distribution conditions. The method further includes training, associated with the update to the group of local data instances, the machine learning model.

Abstract: Techniques related to using an anticipatory NACK for a decoding failure prediction are disclosed. Some aspects of the disclosure relate to devices and methods for wireless communication including obtaining downlink control information (DCI) including scheduling information for a data transmission over one or more data channel resources. When a predictive channel model predicts a high probability of a decoding failure for the one or more data channel resources, based on the high probability of decoding failure, an anticipatory NACK may be transmitted, indicating an anticipated failure to properly decode the data transmission. Other aspects, embodiments, and features are also claimed and described.

Abstract: Aspects of the present disclosure include methods, apparatuses, and computer readable media for receiving interference information relating to at least one of a scheduling granularity, a subcarrier spacing, a scheduler type, resource utilization, a number of active UEs, a number of active beams, an elevation and an azimuth angle of each of the number of active beams, a synchronization signal block index, or a semi-persistent scheduling configuration associated with at least one neighbor base station (BS), estimating or predicting, based on the interference information received, an interference power profile caused by the at least one neighbor BS, receiving at least one of data information or control information, and decoding the at least one of the data information or the control information based on the interference power profile.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may transmit a corruption configuration indication that specifies a corruption parameter associated with generating controlled corrupted information. The network node may receive the controlled corrupted information that is based at least in part on the corruption parameter. Numerous other aspects are described.

Abstract: Certain aspects of the present disclosure provide a method for wireless communications at a first network entity, comprising: outputting, to a second network entity, a request for an evaluation of suitability of at least one user equipment (UE) for data collection for at least one of machine learning (ML) model training or scenario evaluation; and obtaining, in response to the request, results of the evaluation.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit, to a network node, an indication of a local training data distribution associated with the UE. The UE may transmit, to the network node, local gradient information for a federated learning model that is based at least in part on the local training data distribution associated with the UE. Numerous other aspects are provided.

Abstract: Disclosed are methods and systems for monitoring control channel candidates in a common search space (or a common search space set) in a wireless communication network. One of the methods is performed at a user equipment (UE). The method comprises: determining a first set of parameters for configuring a first set of monitoring occasions for the UE to monitor control channel candidates in a common search space (CSS); transmitting, to a network entity, an indication indicating the first set of parameters; and monitoring control channel candidates in the CSS during the first set of monitoring occasions.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, a network entity may obtain information (e.g., a data set, a model update) corresponding to a user equipment (UE), the information associated with a machine learning model. The network entity may determine whether the information or the UE providing the information is trusted or untrusted based on the information. The network entity may output, to another network entity, an indication that the information corresponding to the UE is considered untrusted or trusted based on a predicted output of the machine learning model (e.g., if the model is trained using the information). The other network entity may further train the machine learning model using trusted information and may refrain from using untrusted information. Additionally, or alternatively, if a UE is determined to be untrusted, a network entity may configure the UE to refrain from further data collection processes.