Abstract: Methods, systems, and devices for wireless communications are described. In some systems, a user equipment (UE) may receive, from a base station, a control message including an indication associated with a configuration for an interference measurement (IM) report in a channel state information (CSI) report. The UE may determine a setting of the configuration for the IM report based on the indication, which may include one or both of a resource setting associated with IM information in a frequency domain or a spatial setting associated with IM information in a spatial domain. The UE may generate the IM report based on one or both of the resource setting or the spatial setting and may transmit the CSI report including the IM report to the base station.

Abstract: Aspects presented herein may enhance the accuracy and/or latency of UE positioning based on crowd-sourcing, where a network entity may compute a position estimate of a UE based on neighbor-cell scan data from the UE and one or more reference UEs. In one aspect, a network entity receives a first set of measurements associated with at least one cell from a UE. The network entity performs a position estimation of the UE based on at least one of the first set of measurements associated with the at least one cell, a second set of measurements for each of a set of reference UEs, or a location of each of the set of reference UEs via an ML model, where the UE and the set of reference UEs include at least one common cell.

Abstract: Disclosed are techniques for wireless positioning. In an aspect, a base station determines a channel profile for a multipath channel between the base station and a user equipment (UE) based on at least one positioning reference signal transmitted to the UE or received from the UE by the base station on one or more radio beams, compresses the channel profile into a compressed representation of the channel profile, and transmits the compressed representation of the channel profile to a network entity. The network entity receives the compressed representation of the time-angle channel profile and determines a location of the UE based on the compressed representation of the channel profile.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a configuration for a reference signal used for data collection and/or a first message that indicates that data collection based on the reference signal is allowed. The UE may receive the reference signal. The UE may collect data based at least in part on the reference signal. The UE may transmit the data that is collected. Numerous other aspects are described.

Abstract: In a wireless communication system, a user equipment (UE) vendor and a network entity vendor may configure one or more UEs and network entities to perform data collection, and to train for channel status information (CSI) feedback (CSF) models.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a first network node receives one or more request location information messages from a network entity, wherein the one or more request location information messages configure the first network node to use machine learning to derive one or more features of a wireless channel between the first network node and a second network node, and transmits one or more provide location information messages to the network entity, wherein the one or more provide location information messages include the one or more features of the wireless channel, and wherein the one or more features of the wireless channel are derived based on a machine learning model.

Abstract: Some examples of the techniques described herein may provide encoding or decoding approaches to generate vectors that meet an orthogonality condition in vector compression procedures (e.g., vector compression or decompression). Encoder layers (e.g., neural network layers) on the encode side may generate orthogonal compressed vectors or decoder layers (e.g., neural network layers) on the decode side may generate orthogonal decompressed vectors. For instance, a neural network layer may be trained to generate a compressed or decompressed vector that satisfies an orthogonality condition relative to one or more previously compressed or decompressed vectors in accordance with a dependency order or a compression sequence. An indication of the dependency order may be communicated between the encode side and the decode side to order encoder or decoder layers (e.g., to execute encoder or decoder layers in the compression sequence) to produce compressed or decompressed vectors that satisfy an orthogonality condition.

Abstract: Methods, systems, and devices for wireless communications are described. A first wireless device may be configured to communicate signaling with a second wireless device, an additional wireless device, or both, and perform, based on the signaling, one or more inferences using a machine learning model. The first wireless device may transmit, to the second wireless device, an indication that the machine learning model was applicable for performing the one or more inferences, and receive, from the second wireless device, control signaling indicating that the machine learning model is applicable for communications that are associated with a first set of conditions associated with the communication of the signaling, where the control signaling indicates a model identifier (ID) associated with the machine learning model, that the first set of conditions is associated with the machine learning model, or both.

Abstract: A communications transceiver system that employs self-interference mitigation techniques can detect static objects by using techniques that introduce angular or Doppler diversity. This can include moving Tx and Rx antennas and/or performing beam sweeping. When processing RF sensing data from reflected RF signals, self-interference mitigation techniques can be used and compensation can be made for the movement and/or beam sweeping to allow for both self-interference mitigation and detection of static objects.

Abstract: Aspects of the disclosure relate to a cloud-based parallel processing and cognitive learning computing platform. A computing platform may receive query data comprising a plurality of requested metrics. The computing platform may select a secondary node to process the query request from a plurality of secondary nodes of the computing platform. The computing platform may send the query request to the secondary node. The computing platform may send a request to generate a metric mapping table for a first requested metric to a cognitive learning module of the computing platform. The cognitive learning module may generate the metric mapping table for the first requested metric using neuron clusters that comprise a plurality of unique programmable quadratic function neurons. The computing platform may calculate a value for the first requested metric based on the metric mapping table.

Abstract: Techniques are provided herein for signaling path selection modes for positioning. An example method of signaling a path selection mode includes obtaining one or more signal measurement values for a reference signal, determining one or more propagation paths based on the one or more signal measurement values and the path selection mode, and report the one or more propagation paths and the path selection mode to a location server.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may communicate signaling with a network entity, the signaling indicating a first bandwidth size and a second bandwidth size associated with a projection parameter (W1) and a compression parameter (W2), respectively. The second bandwidth size may be less than the first bandwidth size. In some cases, the UE may determine W1 and W2 based on a first machine learning (ML) model and a second ML model, respectively. The UE may project a portion of received channel state information (CSI) associated with the first bandwidth size onto a sub-space defined by W1 and may compress a portion of the projection associated with the second bandwidth size based on W2. The UE may transmit channel state feedback including the projection, the compression of the projection, a compression of W1, a compression of W2, or any combination thereof.

Abstract: Certain aspects of the present disclosure provide techniques for wireless communications by an apparatus. A method includes sending signaling indicative of one or more power ratios, wherein each of the one or more power ratios is based on a transmit power for transmitting sounding reference signal (SRS) using a corresponding antenna and a receive power for receiving channel state information reference signal (CSI-RS) using the corresponding antenna, and wherein the one or more power ratios are associated with one or more antennas; sending a first CSI-RS; receiving a first SRS; receiving first channel state feedback (CSF) corresponding to the CSI-RS; and performing channel estimation based on the first CSF and the first SRS.

Abstract: Disclosed are techniques for addressing relation round trip time (RTT) positioning and timing advance (TA) command with user equipment (UE) receive-transmit (Rx-Tx) measurement reporting in wireless network such as in new radio (NR).

Abstract: This disclosure provides systems, methods, and devices for wireless communication that support machine learning (ML) positioning model monitoring and life cycle management. In some aspects, a user equipment (UE) may receive, from a network entity, a configuration message associated with a first set of reference signals and a second set of reference signals. The UE may perform first measurements based on the first set of reference signals received from a first set of transmit/receive points (TRPs) to generate first measurement data. The UE may perform second measurements based on the second set of reference signals received from a second set of TRPs to generate second measurement data. The UE may transmit, to the network entity, a reporting message based on the second measurements or based on the first measurement data and the second measurement data. Other aspects and features are also claimed and described.

Abstract: Methods, systems, and devices for wireless communications are described. A first device and a second device may communicate via a channel. The first device may generate and transmit a reference signal, which may be a distortion probing reference signal with a high peak to average power ratio. In one implementation, the first device may use the reference signal as an input for a neural network model to learn a nonlinear response of the second device transmission components. In another implementation, the second device may sample the generated reference signal, and use the samples as inputs for a neural network model to learn the nonlinear response. The first device and the second device may exchange signaling based on learning the nonlinear response, and each device may compensate for the nonlinear response when communicating via the channel.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client may select, based at least in part on a classifier, an autoencoder of a set of autoencoders to be used for encoding an observed wireless communication vector to generate a latent vector. The client may transmit the latent vector and an indication of the autoencoder. Numerous other aspects are provided.

Abstract: Wireless communication devices, systems, and methods related to power saving, including during connected mode operation and for extended reality (XR) data communications with or without discontinuous reception (DRX), are provided. For example, a method of wireless communication includes receiving, while in a connected mode, a configuration based on data traffic for the wireless communication device, the configuration indicating: a first zone associated with a first set of operating parameters for the wireless communication device; and a second zone associated with a second set of operating parameters for the wireless communication device, the second set of operating parameters being different than the first set of operating parameters; operating in the first zone with the first set of operating parameters to monitor for a first downlink communication signal; and operating in the second zone with the second set of operating parameters.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, based at least in part on one or more performance metrics associated with an artificial intelligence (AI) model associated with wireless communication, assistance information associated with an expected performance of the AI model. The UE may assess, based at least in part on the assistance information, the expected performance of the AI model. Numerous other aspects are described.

Abstract: A configuration to allow a base station to be synchronized with an application server to enable the base station to align uplink transmissions of a UE with downlink reception periods of the UE. The base station communicates with a UE using periodic uplink traffic bursts and periodic downlink traffic bursts. The base station selects a time offset to at least one of uplink traffic or downlink traffic to increase an overlap between the uplink traffic bursts and the downlink traffic bursts. The base station sends the time offset to an AF.