Abstract: Certain aspects of the present disclosure generally relate to techniques for selecting a base graph to be used for wireless communications. Selection can be based on a variety of factors. A base graph can be used to derive a low-density parity-check (LDPC) code used for encoding a retransmission of an original transmission. An exemplary method generally includes selecting, based on a modulation and coding scheme (MCS) and a resource allocation (RA) for transmitting a codeword, a base graph (BG), from which to derive a low density parity check (LDPC) code for use in encoding data bits in the codeword (e.g., encoding data bits of a bitstream such that some redundant bits are included in the codeword), encoding the data bits to generate the codeword using the LDPC code derived from the selected BG, and transmitting the codeword using the MCS via resources of the RA.

Abstract: A transmitting device applies a first MCS to a first set of data tones that overlaps with a first set of PRTs within a plurality of tones, the first set of PRTs being associated with a first PAPR reduction signal. The transmitting device applies a second MCS to a second set of data tones that overlaps with a second set of PRTs within the plurality of tones, the second set of PRTs being associated with a second PAPR reduction signal. The transmitting device can transmit a transmission signal comprising the first set of data tones and the second set of data tones, the transmission signal using a waveform based at least in part on the first PAPR reduction signal and the second PAPR reduction signal.

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 configuration for a reference signal used to determine a non-linear behavior of transmission components at a transmitting device may be determined. The configuration for the reference signal may be determined based on signaling transmitted by the transmitting device, signaling transmitted by a device that receives the reference signal, or both. Additionally, or alternatively, the configuration for the reference signal may be determined based on a configuration of other signals transmitted by the transmitting device prior to or concurrently with the transmission of the reference signal. The determined configuration may be used to generate and transmit the reference signal or to determine a configuration of a received reference signal. In both cases, a non-linear response of transmission components at the transmitting device may be determined based on the reference signal.

Abstract: Bandwidth part (BWP) configurations supporting various communication approaches (e.g., full duplex and/or half duplex operations) are described. Full duplex (FD) frequency-based BWP configurations may, for example, be configured as a subset of defined BWP resources for supporting full duplex operation by base stations and/or user equipments (UEs). Usable bandwidth of a FD frequency-based BWP configuration may be selected from half duplex frequency-based BWPs in legacy BWPs. Bandwidths of usable BWPs for a FD frequency-based BWP configuration may be selected so as to be non-overlapping in frequency. Transition between configurations and modes (e.g., between full duplex frequency-based BWP configurations, between half duplex and full duplex modes, etc.) may be managed to avoid periods in which a communication device cannot perform any uplink or downlink transmissions due to switching between defined BWP configurations, or otherwise reduces BWP switching time.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive multiple reference signals (RSs). The UE may measure the multiple RSs to generate measurements for the multiple RSs. The UE may encode the measurements using a machine learning model to generate codewords representing a quantized version of the measurements. The UE may transmit the codewords. Numerous other aspects are described.

Abstract: Techniques for peak-to-average power ratio (PAPR) reduction are described. Wireless devices may use one or more PAPR shaping resources, such as expanded bandwidth and/or pulse-shaping filtering, for shaping a signal to reduce PAPR. For example, expanded bandwidth may be utilized for adding a cyclic affix (CA), such as may comprise a cyclic prefix (CP), cyclic suffix (CS), etc., and combinations thereof, to a frequency domain data signal to provide a CP augmented frequency domain data signal used to generate a reduced PAPR time domain data signal. Additionally or alternatively, pulse-shaping filtering may be applied to a frequency domain signal to provide a pulse-shaped frequency domain data signal used to generate a reduced PAPR time domain data signal. 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 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: Disclosed are systems, apparatuses, processes, and computer-readable media for wireless communications. For example, an example of a process includes receiving a signal comprising a slot including at least one shared signal resource. The process further includes determining whether the slot is allocated for sensing and communications or is allocated for communications. The process further includes determining whether to demodulate the at least one shared signal resource using at least one sensing reference signal (RS) resource or at least one communication RS based whether the slot is allocated for sensing and communications or is allocated for communications.

Abstract: The techniques described herein utilize a machine learning algorithm to train the encoders from multiple UE vendors and a shared decoder from a gNB vendor in order to develop a universal gNB decoder that may be capable of decoding input from UEs from different UE vendors at comparable performance and overhead to different decoders that are specifically developed for each encoder.

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: In an aspect, a wireless node may determine a sub-panel configuration associated with a reconfigurable intelligence surface (RIS) that includes a plurality of sub-panels. The wireless node may transmit or receive one or more signals via one or more sub-panels of the plurality of sub-panels in accordance with the sub-panel configuration.

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: Various aspects of the present disclosure relate to wireless communication. In some aspects, a client may receive an observed environmental vector feedback configuration associated with a reporting procedure for reporting updates corresponding to at least one observed environmental vector that is based at least in part on one or more features associated with an environment of the client, wherein the observed environmental vector comprises input for a first machine learning component that determines client specific parameters for use by a second machine learning component. The client may transmit an update corresponding to the at least one observed environmental vector based at least in part on the observed environmental vector feedback configuration. Numerous other aspects are provided.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may train a first set of layers of a neural network based on channel estimates using a set of resources. The UE may generate a set of weights for the first set of layers of the neural network based on the training. The UE may receive, from a first network entity, an indication of an association between a first set of signaling and a second set of signaling based on the first set of layers of the neural network. The UE may receive the second set of signaling from a second network entity and process the second set of signaling using the set of weights for the first set of layers based on the association between the first set of signaling and the second set of signaling.

Abstract: Radio frequency (RF) sensing by a sensing entity with an oscillator that introduces frequency errors is supported using dual direction bistatic sensing. The sensing entity transmits a sensing signal that is reflected by an object and received by a second sensing entity, which measures a first frequency offset that includes an oscillator error from the transmission of the sensing signal and a Doppler shift from the object. The sensing entity also receives and measures a second frequency offset of a sensing signal transmitted by the second sensing entity and reflected by the object, which includes a second frequency offset that includes an oscillator error from the reception of the sensing signal and a Doppler shift from the target object. The velocity of the object may be estimated based on a combination of the first and second frequency offsets, which cancels the oscillator error caused by the sensing entity.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a transformer configuration that includes a transmitter neural network configured to be used to generate at least one latent vector corresponding to one or more computation tasks of a plurality of computation tasks associated with a transformer-based cross-node machine learning system. The UE may transmit the at least one latent vector based at least in part on instantiating the transmitter neural network. Numerous other aspects are described.

Abstract: The present disclosure provides for adaptive resource management in new radio operations that adapts a numerology including a subcarrier spacing and/or cyclic prefix for a user equipment (UE) traveling at a high speed. A base station may transmit via a plurality of remote radio heads (RRH) to a user equipment (UE) is moving along a high speed track. The base station may transmit, in a first time period, using a first numerology including a first subcarrier spacing and a first cyclic prefix ratio, a first transmission for the UE. The base station may transmit, in a subsequent time period, using a second numerology including a second subcarrier spacing and a second cyclic prefix ratio, a second transmission for the UE. At least one of the second subcarrier spacing is different than the first subcarrier spacing or the second cyclic prefix ratio is different than the first cyclic prefix ratio.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may receive, using at least one lower layer of a wireless communication protocol stack, a machine learning component from a server device. The client device may transmit, to the server device and using the at least one lower layer, an update associated with the machine learning component, wherein transmitting the update comprises transmitting a plurality of transport blocks. Numerous other aspects are described.

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.