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: 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: 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: 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.

Abstract: Methods, systems, and devices for wireless communication are described. A wireless device may transmit feedback, such as hybrid automatic repeat request (HARQ) feedback for groups of code blocks rather than for an entire transport block or individual code blocks. The wireless device may transmit an acknowledgement (ACK) or negative-acknowledgement (NACK) to provide feedback for each code block group of a set of code block groups. An ACK may indicate that code blocks in a code block group were successfully decoded, and a NACK may indicate that at least one code block in a code block group was not successfully decoded. Wireless devices may support several techniques for grouping code blocks for feedback reporting to allow for efficient retransmissions and limited overhead. Different grouping schemes may be employed depending on system constraints, device capability, link conditions, or the like.

Abstract: Methods, systems, and devices for wireless communication are described. A network entity may monitor a performance of a machine learning (ML) model or ML model-based functionality associated with a user equipment (UE). The UE may receive one or more control messages that indicate a life cycle management (LCM) operation for the ML model or ML model-based functionality. The one or more control messages may include an indication of whether the LCM operation is based on the performance of the MIL model or ML model-based functionality. In some examples, the indication may include or be an example of a performance report associated with the performance of the ML model or ML model-based functionality. The UE may perform the LCM operation for the ML model or ML model-based functionality. The UE or the network entity may transmit the indication to a server associated with the ML model or ML model-based functionality.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a wake up signal (WUS) within a WUS monitoring occasion (WMO) indicating to initiate a discontinuous reception (DRX) on-duration, a time to initiate the DRX on-duration being based at least in part on a timing of the WMO within a set of WMOs. The UE may receive a communication based at least in part on initiating the DRX on-duration at the time. Numerous other aspects are described.

Abstract: A second base station may allocate a set of downlink resources for a downlink transmission. The set of downlink resources may include a subset of null resources, which may serve as a signature for the second base station. The second base station may transmit downlink data or reference signals on the set of downlink resources including the subset of null resources. A first UE may experience the downlink transmission from the second base station as interference. The first UE may identify the set of downlink resources. The first UE may identify the subset of null resources. The first UE may transmit, to a first base station, an indication of the subset of null resources. The first base station may identify the second base station based on the indication of the subset of null resources. The first base station may transmit, to the second base station, an interference coordination message.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may be receive a grant configuration including multiple set of parameters, and the UE may select a set of parameters from the configured grant based on a channel condition. In some examples, the UE may be configured to signal to a network (e.g., a base station) the selected set of parameters and their corresponding parameter values. For example, the UE may be configured to signal the selected set of parameters over semi-static signaling (e.g., radio resource control (RRC) messaging) or dynamic signaling (e.g., medium access control-control element (MAC-CE) messaging, uplink control information (UCI) messaging, or the like). By configuring the UE with multiple set of parameters, the UE may adapt to changing channel conditions, which may provide higher reliability and lower latency for wireless communications, as well as reduce power consumption for the UE.

Abstract: Disclosed are techniques for wireless positioning. In an aspect, a user equipment (UE) may send, to a first entity, first information indicating a maximum number of positioning signal transmissions or a maximum power for positioning signal transmissions that the UE can support. The UE may receive, from the first entity, configuration information for changing a positioning mode of the UE to a positioning mode selected from a first positioning mode and a second positioning mode. The UE may perform a positioning operation according to the selected positioning mode. In some aspects, the first positioning mode is a normal positioning mode in which the UE sends UL positioning reference signals to a plurality of TRPS, and the second positioning mode is a UL power-limited mode in which the UE sends UL positioning reference signals to only one or a small number of TRPs.

Abstract: Methods, systems, and devices for wireless communication are described. A network node may determine a first time location associated with a peak in data traffic for multiple devices in communication with a communications network including the network node. The network node may determine a second time location for the peak in the data traffic for a subset of the devices based on a threshold for an overall peak in data traffic for the multiple devices. The network node may transmit a signal that indicates the second time location for the peak in the data traffic for the subset of devices. The network node may communicate the data with the subset of devices based on the signal indicating the second time location.