Abstract: A user equipment (UE) receives, from a network entity, a message indicating a change in a set of downlink beams for channel state information reference signals (CSI-RSs), and a context associated with the change. The UE saves state values in an auto-encoder neural network in response to receiving the message and associates the saved state values in the auto-encoder neural network to the context in the received message. The UE also resets the state values in the auto-encoder neural network in response to receiving the message and estimates a channel state based on the CSI-RSs received on the changed set of downlink beams. The UE compresses the channel state with the auto-encoder neural network based on the reset state values and further sends to the network entity, the compressed channel state.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a user equipment (UE) applies a plurality of positioning reference signal (PRS) processing windows to a PRS resource received from a network node over a multipath channel, determines a plurality of channel estimates for the PRS resource based on the plurality of PRS processing windows, and determines a positioning measurement of the PRS resource based on the plurality of channel estimates.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a user equipment (UE) applies a plurality of positioning reference signal (PRS) processing windows to a PRS resource received from a network node over a multipath channel, determines a plurality of channel estimates for the PRS resource based on the plurality of PRS processing windows, and determines a plurality of positioning measurements of the PRS resource based on the plurality of channel estimates.

Abstract: Certain aspects of the present disclosure provide techniques for transmitting and processing channel state information (CSI) reference signals (CSI-RS). An exemplary method includes determining a configuration of channel state information reference signals (CSI-RSs), wherein the configuration indicates a set of resource elements (REs) to be used for CSI-RSs and a first mapping of CSI-RS ports to the set of REs; sending an indication of the configuration of the CSI-RSs; and transmitting the CSI-RSs according to the determined configuration.

Abstract: Aspects of the present disclosure provide a wireless device that communicates with another wireless device utilizing self-contained subframes. The wireless device communicates with a scheduling entity utilizing a plurality of self-contained subframes that include a first subframe and a second subframe. Each of the self-contained subframes includes an uplink (UL) portion and a downlink (DL) portion. The wireless device further receives DL control information from the scheduling entity in the DL portion of the first subframe, and transmits UL data that includes a plurality of reference signal bursts to the scheduling entity in the UL portion of the first subframe. The plurality of reference signal bursts are uniformly spaced in at least a portion of the UL portion of the first subframe.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may perform a measurement operation to attain multiple measurements to report to a base station. The measurements may correspond to a first number of bits if reported. The UE may compress the measurements using an encoder neural network (NN) to obtain an encoder output indicating the measurements. This encoder output may include a second number of bits that is less than the first number of bits. The UE may report the encoder output to the base station in this compressed form. At the base station, the encoder output may be decompressed according to a decoder NN. Once the base station decompresses the encoder output, the UE and base station may communicate according to the measurements determined from the decompression. In some cases, the base station may perform load redistribution based on the measurements.

Abstract: A network entity may transmit a configuration for neural network training parameters for wireless communication by the UE, and the UE may train the neural network at the UE based on the configuration received from the network entity. The network entity may transmit a training command in a wireless message to the UE, and the UE may train the neural network based on the received configuration in response to the received training command. The configuration may include a period of time associated with the training the neural network. The period of time may indicate an action for the UE to perform when the period of time expires, and/or indicate the periodicity of the neural network training.

Abstract: Generally, the described techniques provide for duplex mode configuration for control information (e.g., duplex mode configuration for communication of downlink control information). For example, duplex modes may be defined (e.g., as full duplex (FD) capable, FD only, half duplex (HD) only, etc.), and such duplex modes may be configured for control resource sets (CORESETs), search space (SS) sets, or both. In some examples, duplex modes configured for a CORESET/SS set may depend on the priority of information conveyed via the CORESET/SS set. For example, a CORESET/SS set for high priority or critical control information may be configured with a HD mode to avoid FD self-interference, while CORESET/SS set for other control information may be configured with a FD mode to improve spectrum efficiency. Techniques for resolving conflicting duplex mode configurations (e.g., when a HD mode is configured for a SS set within a CORESET configured with FD) are also described.

Abstract: Wireless communications systems and methods related to bandwidth-efficient utilization of peak reduction tone resource are provided. An example method of wireless communications performed by a user equipment (UE) includes receiving, from a base station in a first subband, a resource allocation indicating a set of transmission tones comprising a set of first data tones, a set of second data tones and a set of peak reduction tones (PRTs). The resource allocation can indicate the set of first data tones overlaps at least a portion of the set of PRTs at respective locations of a set of PRT locations and the set of second data tones is arranged at a set of data tone locations within a particular bandwidth. The UE can communicate, with the base station in a second subband, a data transmission using an outgoing waveform based at least in part on the resource allocation.

Abstract: In an aspect, a UE obtains information (e.g., UE-specific information) associated with a set of triggering criteria (e.g., from a server, a serving network, e.g., in conjunction with or separate from a set of neural network functions) for a set of neural network functions. The UE obtains positioning measurement data associated with a location of the UE, and processes the positioning measurement data into a respective set of positioning measurement features based at least in part upon the positioning measurement data and at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria. The UE reports the processed set of positioning measurement features to a network component (e.g., BS, LMF, etc.).

Abstract: Wireless communication techniques that include techniques for allocating resources for peak reduction tones are discussed. A UE may receive from a base station an indication of one or more frequency resources that are allocated for uplink communication. The UE may also receive from the base station an indication of a subset of the one or more frequency resources allocated for uplink communication that are also allocated for transmission of one or more peak reduction tones. The UE may transmit to the base station at least one peak reduction tone on at least one frequency resource of the subset of the one or more frequency resources. Other aspects and features are also claimed and described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client may determine, using a first client autoencoder, a feature vector associated with one or more features associated with an environment of the client. The client may determine a latent vector using a second client autoencoder and based at least in part on the feature vector. The client may transmit the feature vector and the latent vector. Numerous other aspects are provided.

Abstract: Various embodiments include methods performed in receiver circuitry of a wireless communication device for demodulating wireless transmission waveforms to reconstruct data tones, which may include receiving, from a transmitter, wireless transmission waveforms that includes peak reduction tones (PRTs) that were inserted by a PRT neural network in the transmitter, and demodulating the received wireless transmission waveforms using a decoder neural network that has been trained based on outputs of the transmitter to output a reconstruction of the data tones. Further embodiments include exchanging information between the transmitter and receiver circuitry to coordinate the PRT neural network used for inserting PRTs in the transmitting wireless communication device and the decoder neural network used in the receiving wireless communication device for demodulating transmission waveforms received from the transmitting wireless communication device.

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for reporting configurations for neural network-based processing at a UE. A network entity may transmit to the UE a CSI configuration that includes one or more parameters for a neural network and one or more reference signals. The UE may measure the one or more reference signals based on the CSI configuration. A CSI may be based on the one or more parameters and the measurement of the one or more reference signals. The UE may report the CSI to the network entity based on output of the neural network.

Abstract: A first wireless device, such as a user equipment, generates a message indicating a processing time for at least one of training a neural network for channel state information (CSI) derivation or for reporting the CSI based on a trained neural network. The first wireless device transmits the message indicating the processing time to a second wireless device. The second wireless device may be a network entity, such as a base station, a transmission reception point, or another UE.

Abstract: Certain aspects of the present disclosure provide techniques for joint communication and radar sensing. A method is provided for wireless communications by a network entity. The method generally includes communicating one or more radar signals in a first set of slots. Each of the first set of slots comprises an extended cyclic prefix have a first length. The method generally includes communicating one or more signals in a second set of slots, each of the second set of slots comprising a normal cyclic prefix having a second length that is shorter than the first length.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive an indication of a plurality of selectable physical resource block (PRB) bundle sizes. The UE may receive downlink control information (DCI) that does not include a PRB bundling size indicator that indicates a PRB bundle size to be selected from the plurality of selectable PRB bundle sizes. The UE may select a PRB bundle size, from the plurality of selectable PRB bundle sizes, despite the DCI not including the PRB bundling size indicator. Numerous other aspects are provided.

Abstract: Various aspects of the present disclosure relate to wireless communication. In some aspects, a client may receive a selection feedback configuration associated with a reporting procedure for reporting updates corresponding to at least one autoencoder index associated with one or more autoencoders selected by the client. The client may determine an update corresponding to the at least one autoencoder index. The client may transmit the update based at least in part on the selection feedback configuration. Numerous other aspects are provided.

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 first device may generate a multi-part neural network based channel state information feedback (CSF) message that comprises: a first part that indicates contents of a second part, and the second part; and transmit the multi-part neural network based CSF to a second device. Numerous other aspects are provided.