Abstract: Disclosed are techniques for wireless sensing. In an aspect, a user equipment (UE) measures at least a line-of-sight (LOS) path and a non-line-of-sight (NLOS) path of a first downlink positioning reference signal (DL-PRS) from a first transmission-reception point (TRP), measures at least an LOS path and an NLOS path of a second DL-PRS from a second TRP, measures at least an LOS path and an NLOS path of a third DL-PRS from a third TRP, and enables a location of a non-participating target object to be determined based, at least in part, on reference signal time difference (RSTD) measurements between a time of arrival (ToA) of the LOS path of the first DL-PRS and the ToAs of the NLOS paths of the first, second, and third DL-PRS. In an aspect, the non-participating target object does not participate in determining its own location.

Abstract: Methods, systems, and devices for wireless communications are described in which a base station may identify a null space matrix that lies within a null space of an effective channel matrix for communications between the base station and a user equipment (UE). An indication of the null space matrix may be provided to the UE, and the null space matrix used to determine modifications to a precoding matrix. The base station and UE may determine a redistribution matrix that provides a reduced variance of transmission powers for a number of transmission channels, where a product of the null space matrix and the redistribution matrix may be computed and added to the precoding matrix to generate a modified precoding matrix. The modified precoding matrix may be used to generate the communications from the base station and UE with reduced power variance across channels.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) having partially coherent antennas may be configured for simultaneous transmissions on groups of antennas. To achieve the benefits of simultaneous transmissions using groups of antenna that are partially coherent, without having the transmissions affect each other, the UE may apply a hybrid closed-loop multiple-input multiple-output (MIMO) scheme among each antenna in the antenna groups where phase coherence can be maintained. Following the hybrid closed-loop MIMO scheme, the UE may apply a transparent diversity scheme across each antenna of the groups. Alternatively, the UE may first apply the transparent diversity scheme and next apply the hybrid closed-loop MIMO scheme. By applying a hybrid closed-loop MIMO scheme, and a transparent diversity scheme, the UE may fully realize its resources and contribute to an improved spatial diversity for a MIMO system.

Abstract: Disclosed are techniques for determining tone patterns for transmission of reference signals. One or more parameters associated with communications with a base station over a time duration, may be determined by a user equipment. The user equipment may determine, based on the one or more parameters, a tone pattern for a reference signal for use in communications with the base station over a future time duration. The user equipment may then transmit the tone pattern to the base station. The user equipment may then receive the reference signal from the base station, over the tone patterns.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may determine a feedback associated with a machine learning component based at least in part on applying the machine learning component. Accordingly, the client device may transmit a quantized value based at least in part on the feedback. The quantized value is determined based at least in part on distances between the feedback and a non-uniform set of quantized digits. Numerous other aspects are provided.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first network node may receive a signal associated with a set of resources. The first network node may determine, using a multi-head machine learning model having a body module and a plurality of head modules, a plurality of estimated parameter values of a plurality of parameters corresponding to the set of resources, wherein the body module extracts a set of common features based at least in part on a set of model inputs corresponding to the set of resources, and wherein the plurality of head modules generate the plurality of estimated parameter values based at least in part on the set of common features. The first network node may perform a wireless communication operation based at least in part on the plurality of estimated parameter values. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client may receive a customization feature vector feedback configuration associated with a reporting procedure for reporting updates corresponding to at least one customization feature vector that is based at least in part on one or more features associated with an environment of the client. The client may determine an update corresponding to the at least one customization feature vector using a machine learning component. The client may transmit the update based at least in part on the customization feature vector feedback configuration. Numerous other aspects are provided.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first device may transmit a neural network capability indication that indicates a capability of the first device associated with training at least one channel state feedback (CSF) neural network for facilitating providing CSF. The first device may receive, based at least in part on the capability of the first device, a CSF neural network configuration that indicates at least one parameter associated with the at least one CSF neural network. Numerous other aspects are provided.

Abstract: A method of wireless communication by a user equipment (UE) includes receiving, from a network device, a request to initiate gradient computations for a round of federated learning. The method also includes computing gradients in response to receiving the request to initiate gradient computation. The method further includes informing the network device of availability of the gradients. The method still further includes receiving, from the network device, information to enable transfer of the gradients to the network device. The method further includes transferring the gradients to the network device in response to receiving the information to enable transfer.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment may receive a resource allocation indicating a plurality of transmission tones comprising a subset of data tones of a plurality of data tones and a subset of peak reduction tones (PRTs) of a plurality of PRTs, wherein the resource allocation indicates a plurality of data tone locations and a plurality of PRT locations within a particular bandwidth, wherein a subset of PRT locations of the plurality of PRT locations are arranged relative to a subset of data tone locations of the plurality of data tone locations according to a PRT sequence corresponding to a set of allocated frequency resources; and transmit a data transmission using a waveform based at least in part on the resource allocation. Numerous other aspects are provided.

Abstract: Aspects of the disclosure relate to determining channel state information (CSI) on a component carrier. In an example operation, a device determines a mapping between first time-frequency resources corresponding to a first component carrier (CC) and second time-frequency resources corresponding to a second CC using a prediction algorithm. The device receives, from a base station, a channel state information reference signal (CSI-RS) on the first time-frequency resources corresponding to the first CC and measures first CSI on the first time-frequency resources corresponding to the first CC based on the received CSI-RS. The device further predicts second CSI on the second time-frequency resources corresponding to the second CC based on the measured first CSI using the prediction algorithm. The device then generates a CSI report based on the predicted second CSI and sends the CSI report to the base station.

Abstract: Disclosed are various techniques for wireless positioning. In an aspect, a base station calculates statistics of one or more time-angle metrics based on a signal received from a user equipment, and reports the statistics to a network entity, such as a location server, which uses the statistics to estimate a position of the UE. In some aspects, the network entity receives statistics from multiple base stations, which allows the network entity to more accurately determine the position of the UE. In some aspects, the base station reports the statistics to a network entity according to a statistics reporting configuration, which the network entity may provide to the base station.

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: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may encode channel state feedback (CSF) information to compress the CSF information to a first encoding output associated with a first dimensional space, and apply entropy coding to the first encoding output of the channel state feedback information. The entropy coding may transform the first encoding output to a second encoding output associated with a second dimensional space that is smaller than the first dimensional space of the first encoding output. The UE may transmit a CSF message comprising the second encoding output. A network device may receive the CSF message and apply entropy decoding to the compressed CSF information to partially decompress the compressed CSF information to a first decoding output. The network device may decode the first decoding output to completely decompress the compressed CSF information to a second decoding output.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first network entity may generate cell information about a first cell provided at least in part by the first network entity, the cell information associated with one or more potential sources of interference at a second cell provided at least in part by a second network entity, the one or more potential sources of interference being associated with the first cell. The first network entity may communicate the cell information to the second network entity. Numerous other aspects are described.

Abstract: A protocol stack architecture for processing machine learning (ML) data includes a ML layer to manage ML data communication with a network device. The ML layer is coupled to multiple ML training blocks, and ML and inference blocks for multiple neural networks, and an analog data communications stack coupled to the ML layer. The analog data communications stack has an upper media access control analog (MAC-A) layer coupled to the ML layer and configured to store data for each neural network, a lower MAC-A layer coupled to the upper MAC-A layer and configured to segment and reassemble analog ML data, and an analog physical layer coupled to the lower MAC-A layer and configured to communicate analog data with the network device. The architecture includes a digital data communications stack coupled to the ML layer and the lower MAC-A layer and configured to manage digital communications with the network device.

Abstract: A method of wireless communication by a first network device includes receiving a machine learning model, the machine learning model being unvalidated and trained. The method also includes quantizing and compiling the machine learning model. The method further includes obtaining verification data. The method still further includes validating the machine learning model with the verification data to determine a performance level of the machine learning model. A method of wireless communication by a first network device includes transmitting machine learning model verification data to a second network device. The method also includes receiving a report, from the second network device, indicating performance of a machine learning model with the verification data. The method further includes validating the machine learning model in response to the performance satisfying a performance condition.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first device may receive a request to report updates for one or more weights of a neural network configured for encoding channel state information feedback messages. The first device may transmit a report that indicates the updates for the one or more weights. Numerous other aspects are provided.

Abstract: In an aspect, a network component (e.g., BS, server, etc.) obtains measurement information associated with uplink signal(s) from UE(s), with the uplink signal(s) having reciprocity with one or more downlink beams of wireless node(s) (e.g., TRP, reference UE, etc.). The network component determines (e.g., generates or refines) a measurement (e.g., RFFP-P) model based on the measurement information. The network component provides the measurement (e.g., RFFP-P) model to a target UE. The target UE receives at least one signal (e.g., PRS) on the one or more downlink beams from the wireless node(s). The target UE processes the at least one signal (e.g., predicts target UE location) based at least in part on the measurement (e.g., RFFP-P) model.