Abstract: Various embodiments include methods for demodulating wireless transmission waveforms to reconstruct data tones, performed in receiver circuitry of a wireless communication device. The methods may include receiving time domain wireless transmission waveforms that include time domain peak reduction tones (PRTs) that were generated by a set of PRT neural networks using time domain data signals derived from frequency domain data tones in another wireless communication device, reconstructing the time domain data signals from the time domain wireless transmission waveforms using a receiver neural network that has been trained in conjunction with the set of PRT neural networks to generate a time domain reconstruction of data signals, and transforming the time domain reconstruction of data signals to a frequency domain reconstruction of data tones.

Abstract: Various embodiments include methods for reducing peak to average power ratio of wireless transmission waveforms, performed in transmitter circuitry of a wireless communication. Various embodiments may include receiving frequency domain data tones, transforming the frequency domain data tones to time domain data signals, generating, using a set of peak reduction tone (PRT) neural networks, time domain PRTs using the time domain data signals in which the set of PRT neural networks have been trained in conjunction with an augmentation neural network, and a receiver neural network, generating an output of the augmentation neural network based on an input of final combined time domain signals including the time domain PRTs combined with previous combined time domain signals, and generating time domain wireless transmission waveforms that include the output of the augmentation neural network combined with the final combined time domain signals.

Abstract: Techniques are provided for determining a position of a mobile device. An example method of reporting a probability distribution for positioning a mobile device includes obtaining positioning measurements, determining one or more probability distributions of one or more positioning metrics based on the positioning measurements, determining a parametric representation of the one or more probability distributions, and reporting the parametric representation.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first device may encode a data set using one or more extraction operations and compression operations associated with a neural network, the one or more extraction operations and compression operations being based at least in part on a set of features of the data set to produce a compressed data set. The first device may transmit the compressed data set to a second device. Numerous other aspects are provided.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may transmit a request for sets of neural network weights, each set corresponding to a neural network, to one or more network entities. The one or more network entities may transmit multiple sets of neural network weights to the UE in response to the request. The UE may run a classifier neural network on the sets of neural network weights to select a set of neural network weights corresponding to a neural network. The UE may transmit a neural network identifier to the one or more network entities that transmitted neural network weights.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client may determine, using a conditioning network and based at least in part on an observed environmental vector, a set of client-specific parameters. The client may determine a latent vector using a client autoencoder and based at least in part on the set of client-specific parameters and the set of shared parameters. The client may transmit the observed environmental vector and the latent vector to a server. Numerous other aspects are provided.

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: Methods, systems, and devices for wireless communications are described. Some systems may support the transmission of a predicted negative acknowledgment (NACK) indication for a downlink data message. In some cases, a base station may transmit a downlink data message to a user equipment (UE). The UE may perform a decoding process for the message. The UE may determine whether the decoding process is likely to fail during a first portion of the decoding process prior to completion of the decoding process. The UE may transmit a predicted NACK message to the base station prior to a feedback opportunity configured for the UE to transmit feedback based on a result of the completed decoding process. The base station may determine whether to retransmit the downlink data message based on the predicted NACK message, such that the predicted NACK message may reduce the latency associated with retransmission of downlink data messages.

Abstract: Aspects of the present disclosure describe receiving downlink signaling from an access point, determining, based at least in part on the downlink signaling, one or more precoders for uplink signaling, transmitting, to the access point and based at least in part on the one or more precoders, uplink signaling, and receiving, from the access point and based on transmitting the uplink signaling, a precoder indication to use the one or more precoders or one or more other precoders in transmitting uplink communications to the access point.

Abstract: A discovery measurement timing configuration (DMTC) window reservation signal is disclosed for new radio (NR) shared spectrum (NR-SS) networks. The DMTC window is defined providing a reserved location for transmission of essential control signaling and potentially high priority traffic that may be transmitted in a prioritized manner. Access within the DMTC window may be provided with a clear channel assessment (CCA) exempt transmission (CET) option or a non-CET option. When the CET option is used, the reservation duration and period may be conveyed through the channel reservation signal. User equipments (UEs) that detect the reservation signal may re-transmit with added offset to inform neighboring base station that may not be within range of the serving base station. Base station that receive either the transmitted or re-transmitted reservation signal will refrain from communications that may interfere with the reception at the UEs.

Abstract: In an aspect, a UE obtains information (e.g., UE-specific information, etc.) associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedure, obtains positioning measurement data associated with a location of the UE, and determines a positioning estimate for the UE 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.

Abstract: In an aspect, a network component transmits, to a UE, at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures. The UE may obtain positioning measurement data associated with the UE, and may process the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function. The UE may report the processed set of positioning measurement features to a network component, such as the BS or LMF.

Abstract: In an aspect, a BS obtains at least one neural network function configured to facilitate a UE to derive a likelihood of at least one set of positioning measurement features being present at a candidate set of positioning estimates for the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures. The BS transmits the at least one neural network function to the UE. In another aspect, the UE obtains positioning measurement data associated with a location of the UE (e.g., locally the UE, or remotely from the BS). The UE determines a positioning estimate for the UE based at least in part upon the positioning measurement data and the at least one neural network function.

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 set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedures. 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.

Abstract: Multiple modulation and coding scheme (MCS) tables for new radio (NR) networks is disclosed. Multiple MCS tables are defined with different sets of MCS values selected for improved performance in various transmission conditions. The multiple MCS tables may be known to base stations and user equipments (UEs) in the network. A serving base station may determine a set of transmission characteristics related to a transmission environment of a UE. The base station selects one of the MCS tables of the multiple MCS tables based on the set of transmission characteristics. The selected MCS table includes MCS values that may provide improved performance for the UE considering one or more of the characteristics of the set of transmission characteristics. The base station will then transmit an indication of the selected MCS table to the served UE.

Abstract: In an aspect, first and second UEs each detect a target associated with a deflection point for a transmission beam from a BS to the respective UE, and each transmit, to the BS, information associated with the target. The BS transmits STRS configuration information to the first and second UEs. The BS multicasts, to the first and second UEs, an STRS on the transmission beam in accordance with the STRS configuration information.

Abstract: A method, apparatus, or computer-readable medium with instructions for beam management for radio frequency (RF) sensing at a wireless device. The wireless device performs a first beamsweep of an RF signal and measures a reflection of the RF signal based on the first beamsweep. The wireless device performs a second beamsweep of the RF signal, wherein the first beamsweep is based on a different parameter than the second beamsweep and measures the reflection of the RF signal based on the second beamsweep. The wireless device selects a beam for RF sensing based on the first beamsweep and the second beamsweep.

Abstract: Techniques are provided for managing transmit and receive beams in a millimeter wave (mmW) communication system for use in bistatic radio frequency (RF) sensing. An example method of tracking targets with bistatic radio frequency sensing includes receiving one or more sensing reference signals, generating a signal report based at least in part on the one or more sensing reference signals, transmitting the signal report, receiving tracking signal configuration information, receiving one or more tracking reference signals identified in the tracking signal configuration information, and tracking one or more targets associated with the one or more tracking reference signals.

Abstract: Techniques are provided for single sided beam management in a millimeter wave (mmW) communication system for use in bistatic radio frequency (RF) sensing. An example method of tracking targets with bistatic radio frequency sensing includes receiving a scanning reference signal, generating a scanning signal report indicating one or more target groups associated with the scanning reference signal, transmitting the scanning signal report, receiving tracking signal configuration information indicating a tracking reference signal associated with the one or more target groups, receiving the tracking reference signal identified in the tracking signal configuration information, and tracking the one or more target groups associated with the tracking reference signal.

Abstract: Methods, systems, and apparatuses for uplink descriptor channels are described. An uplink descriptor channel may be used by a user equipment (UE) for providing indications that uplink transmissions over allocated uplink data resources are sent in a configuration different from a configuration indicated by an uplink grant. In various examples an uplink transmission may be transmitted by a UE, and received by a base station, and the uplink transmission may include the uplink descriptor channel with uplink data. An uplink descriptor channel may include indications that uplink control information is included in the uplink transmission, and/or that the uplink transmission employs a number of transmission intervals, a number of channels, and/or a different MCS than those indicated by the uplink grant. A base station may receive the uplink descriptor channel and adjust various parameters of decoding an uplink transmission and/or allocating radio frequency spectrum resources.