Abstract: Aspects described herein relate to determining whether full duplex (FD) communications are configured during resources for communicating one or more messages of a random access procedure, where the FD communications comprising uplink communications and downlink communications occurring in a same frequency band, and responsive to the determining whether the FD communications are configured during the resources, transmitting the one or more messages of the random access procedure using the resources.

Abstract: Various embodiments may employ neural networks at transmitting devices to compress transmit (TX) waveform distortion. In various embodiments, compressed TX waveform distortion information may be conveyed to a receiving device. In various embodiments, the signaling of TX waveform distortion information from a transmitting device to a receiving device may enable a receiving device to mitigate waveform distortion in a transmit waveform received from the transmitting device. Various embodiments include systems and methods of wireless communication by transmitting a waveform to a receiving device performed by a processor of a transmitting device. Various embodiments include systems and methods of wireless communication by receiving a waveform from a transmitting device performed by a processor of a receiving device.

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 locations for the plurality of data tones and locations for the plurality of PRTs within a particular bandwidth, wherein the locations for the plurality of PRTs are arranged relative to the locations for the plurality of data tones according to a PRT subsequence of a universal PRT sequence, and wherein the PRT subsequence corresponds to a sub-band of the particular bandwidth; 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 present disclosure disclose techniques for the indication of the allocation of the downlink (DL) demodulation reference signal (DM-RS) ports for the data channel in NR communications. In some examples, the channel state information (CSI) reference signal (CSI-RS) may be associated with a corresponding DM-RS for a DL data channel. The transmitting device, in some examples, may further transmit a notification that provides resource allocations (e.g., port allocations) that minimize the redundant information that is required to be transmitted from the base station to the user equipment (UE) when the DM-RS port allocation is the same as the CSI-RS port allocation received by the UE in an earlier time slot.

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: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may receive a reporting configuration that indicates one or more reporting conditions, wherein the reporting configuration further indicates that, based at least in part on the one or more reporting conditions being satisfied, the client device is to report an update associated with a machine learning component. The client device may transmit the update associated with the machine learning component to the server device based at least in part on whether the one or more reporting conditions are satisfied. Numerous other aspects are provided.

Abstract: A base station may precode a first reference signal using a first precoder (P1) and at least one second reference signal using at least one second precoder (P2). At least two UEs may measure the first reference signal and the at least one second reference signal, and transmit channel state feedback based on the measuring (m11 and m12 for a first UE, and m21 and m22 for a second UE). The channel state feedback may comprise channel state information compressed by at least one neural network. The base station may derive a higher-level precoder (W) for multiple-input multiple-output (MIMO) downlink transmission to the at least two UEs.

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: 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: 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 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: This disclosure provides systems, methods, and devices for wireless communication that support a reference signal configuration to account for a compression factor due to transmit nonlinearity. In a first aspect, a transmit device is configured to receive an indicator of a scaling factor from a transmit device including a power amplifier. The scaling factor is based on an input power scaling associated with a linear region operation of the power amplifier, an input power scaling associated with a non-linear region operation of the power amplifier, a compression factor, or a combination thereof. The transmit device is further configured to receive shared channel resource elements (REs) from the transmit device during a slot, and recover the received shared channel REs based on the scaling factor. Other aspects and features are also claimed and 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.

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 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 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: A method for wireless communications, comprising: signaling, to a user equipment (UE), an indication of one of at least two rules regarding how quasi co-location (QCL) configured for the UE should be applied for one or more DMRS ports; and sending downlink transmission to the UE with the one or more DMRS ports; receiving signaling from a base station and processing signals received on one or more DMRS ports based on the indicated rule. Said method improves communications between access points and stations in a wireless network.