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: 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: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a signal. The UE may determine, based at least in part on a machine learning component, a predicted communication metric and a confidence indication, the machine learning component comprising a machine learning model, and wherein determining the predicted communication metric and the confidence indication comprises: receiving, by the machine learning model, an input that comprises an input metric and an error measurement corresponding to the input metric; and providing, by the machine learning model, and based at least in part on a machine learning function and the input, the predicted communication metric and the confidence indication. The UE may perform a wireless communication task based at least in part on the predicted communication metric and the confidence indication. Numerous other aspects are described.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a user equipment (UE) transmits one or more provide capabilities messages to a location server, the one or more provide capabilities messages indicating at least a first set of capabilities of the UE to engage in a downlink radio frequency fingerprint (DL-RFFP) positioning procedure, and receives one or more positioning assistance data messages for the DL-RFFP positioning procedure from the location server, the one or more positioning assistance data messages based at least on the first set of capabilities.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a user equipment (UE) receives one or more positioning assistance data messages for a downlink radio frequency fingerprint (DL-RFFP) positioning procedure from a first network entity, the one or more positioning assistance data messages including at least one parameter related to a machine learning model the UE is configured to use for the DL-RFFP positioning procedure, and transmits one or more location information messages to a second network entity, the one or more location information messages including one or more parameters related to the DL-RFFP positioning procedure.

Abstract: In an aspect, a user equipment (UE) transmits an uplink reference signal for positioning (RS-P) to one or more transmission reception points (TRPs). The TRP(s) or an location management function (LMF) extracts features from one or more uplink radio frequency fingerprint (RFFPs) of the uplink RS-P by one or more network components via one or more network-based machine learning (ML) feature extraction models, and sends the extracted features to the UE for position estimation. Other aspects are directed to UE-based round-trip RFFP position estimation session of a UE. Other aspects are directed to network-based round-trip RFFP position estimation of a UE. The UE-based round-trip RFFP position estimation and the network-based round-trip RFFP position estimation may be based on an uplink RFFP (e.g., SRS) of an uplink reference signal for positioning (RS-P) and a downlink RFFP of a downlink RS-P (e.g.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be a user equipment (UE), a base station, or a component thereof. The apparatus may be configured to obtain, using a function, predicted information based on a first set of signals received from a base station via a first subset of a set of beams; determine, based on the predicted information, whether at least one condition associated with the predicted information is satisfied; generate, when the at least one condition is satisfied, beam information associated with at least one first beam of the set of beams based on a second set of signals received from the base station via a second subset of the set of beams; and transmit, to the base station, a report including the beam information.

Abstract: An apparatus may be a user equipment (UE) or a component thereof. The apparatus may be configured to generate measurement information corresponding to a set of signals received from a base station, wherein the set of signals corresponds to a set of beams. The apparatus may be further configured to generate, based on the measurement information, first predicted information, wherein the first predicted information corresponds to at least one beam with which to communicate with the base station. The apparatus may be further configured to determine, based on the first predicted information, that one or more conditions are satisfied. The apparatus may be further configured to transmit, to the base station based on the determination, a first report including second predicted information, wherein the second predicted information includes the first predicted information or the second predicted information is a subset of the first predicted information.

Abstract: An apparatus of the present disclosure may be a network node, such as a user equipment (UE), or a component thereof. The apparatus may be configured to determine a set of values respectively corresponding to a set of signals received from a second network node, and the set of signals corresponds to a set of beams. The apparatus may be further configured to determine, based on the set of values, at least one predicted value, wherein the at least one predicted value corresponds to at least one beam with which to communicate with the second network node. The apparatus may be further configured to transmit, to the second network node, a report, and the report may include information indicating at least one confidence score that is associated with the at least one predicted value.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a first network node determines at least one positioning measurement based on a channel estimate of a positioning reference signal (PRS) transmitted by a second network node, and obtains measurement error feedback for at least the at least one positioning measurement, wherein the measurement error feedback is based on an expected positioning measurement corresponding to the at least one positioning measurement, and wherein the expected positioning measurement is based on a location of the first network node and a location of the second network node.

Abstract: Disclosed are techniques for wireless positioning. In an aspect, a user equipment (UE) may send, to a network entity, information identifying a capability of the UE to process frequency modulated continuous wave (FMCW) signals. The UE may receive, from a network entity, information identifying a set of transmission / reception points (TRPs) for transmitting FMCW signals for a positioning measurement, the set comprising at least a first TRP and a second TRP. The UE may measure a first FMCW signal that was transmitted by the first TRP to produce a first frequency of arrival. The UE may measure a second FMCW signal that was transmitted by the second TRP to produce a second frequency of arrival. The UE may determine a location of the UE based on a frequency difference between the first frequency of arrival and the second frequency of arrival.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a network entity receives a provide location information message from a user equipment (UE), the provide location information message including one or more positioning estimates derived by the UE during one or more positioning inference occasions of a machine learning model, wherein the machine learning model is applied to one or more measurements of a wireless channel between the UE and a network node during each of the one or more positioning inference occasions, and transmits a performance report indicating a performance of the machine learning model at least in deriving the one or more positioning estimates during the one or more positioning inference occasions.

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: Disclosed are techniques for environment sensing. In an aspect, a first network node measures one or more first reference signals on a first carrier frequency, the one or more first reference signals received from a second network node to enable determination of one or more first characteristics associated with one or more target objects, and measures one or more second reference signals on a second carrier frequency, the one or more second reference signals received from the second network node to enable determination of one or more second characteristics associated with the one or more target objects, wherein an accuracy of the one or more second characteristics is higher than an accuracy of the one or more first characteristics based on the one or more first reference signals being measured on the first carrier frequency and the one or more second reference signals being measured on the second carrier frequency.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit capability information that indicates support for one or more model combinations of machine learning (ML) models, wherein the capability information further indicates one or more performance parameters of an ML model of the ML models with respect to a model combination of the one or more model combinations that includes the ML model. The UE may receive one or more indications to use one or more of the ML models based at least in part on the capability information. Numerous other aspects are described.

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: 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.

Abstract: A method of wireless communications by a receiving device includes communicating about content of a known payload with a transmitting device. The method also includes requesting, from the transmitting device, the known payload for training an artificial neural network. The method also receives the known payload in response to the request. The method further performs online training of the artificial neural network with the known payload. A method of wireless communications by a transmitting device includes communicating about content of a known payload with a receiving device and then transmitting an indication informing the receiving device that the known payload will be transmitted. The transmitting device unicasts the known payload to the receiving device for online training of a neural network.

Abstract: A method of wireless communication, by a user equipment (UE), includes determining whether conforming input has been received for each of multiple input branches to a neural network. Each input branch represents a different modality of input features to the neural network. The method also includes replacing an activation with a replacement activation for at least one neural network layer of each of the input branches associated with input determined to be non-conforming input. The method further includes predicting an output parameter based on conforming input received at each input branch and the replacement activation.