Abstract: A method of wireless communication, by a user equipment (UE), includes receiving multiple neural network training configurations for channel state feedback (CSF). Each configuration corresponds to a different neural network framework. The method also includes training each of a group of neural network decoder/encoder pairs in accordance with the received training configurations. A method of wireless communication, by a base station, includes transmitting multiple neural network training configurations to a user equipment (UE) for channel state feedback (CSF). Each configuration corresponds to a different neural network framework. The method also includes receiving a neural network decoder/encoder pair trained in accordance with the training configurations.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive downlink control information (DCI) that includes an indication of a null resource element pattern. The null resource element pattern may be indicated in the DCI using: a value of an antenna port field that also indicates one or more demodulation reference signal (DMRS) ports for the UE and a number of DMRS code-division multiplexing groups without data, or one or more zero power downlink reference signals. The UE may perform one or more demodulation interference measurements based at least in part on the null resource element pattern. The UE may demodulate a downlink communication based at least in part on performing the one or more demodulation interference measurements. Numerous other aspects are provided.

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: Systems and techniques are disclosed to enhance the efficiency of available bandwidth between UEs and base stations. A UE transmits a sounding reference signal (SRS) to the base station. The base station characterizes the uplink channel based on the SRS received and, using reciprocity, applies the channel characterization for the downlink channel. As part of applying the channel information, the base station forms the beam to the UE based on the uplink channel information obtained from the SRS. The UE may include an array of antennas, each UE transmitting a different SRS that the base station receives and uses to characterize the downlink. Multiple UEs (or a single UE with multiple antennas) transmit SRS at the same time and frequency allocation (non-orthogonal), but with each sending its own unique SRS. Further, multiple UEs (or a single UE with multiple antennas) may send their SRS at unique time/frequency allocations (orthogonal).

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: 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: 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: Certain aspects of the present disclosure generally relate to methods and apparatus for sounding reference signal (SRS) guided downlink channel state information-reference signal (CSI-RS) scan. One example method for wireless communication generally includes determining whether to perform channel measurement and reporting operations for a user-equipment (UE) using a reciprocity-based scheme or a channel state information-reference signal (CSI-RS) based scheme based on a criteria of a cell, and performing the channel measurement and reporting operations based on the determination.

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: Certain aspects of the present disclosure relate to methods and apparatus for transmitting multiplexed sounding reference signal (SRS) ports in communications systems operating according to new radio (NR) technologies. An exemplary method includes obtaining an indication of a technique to use in transmitting sounding reference signals (SRS) via five or more antenna ports in one slot and transmitting the SRS via the five or more antenna ports according to the indicated technique.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may perform a measurement operation to attain multiple measurements to report to a base station. The measurements may correspond to a first number of bits if reported. The UE may compress the measurements using an encoder neural network (NN) to obtain an encoder output indicating the measurements. This encoder output may include a second number of bits that is less than the first number of bits. The UE may report the encoder output to the base station in this compressed form. At the base station, the encoder output may be decompressed according to a decoder NN. Once the base station decompresses the encoder output, the UE and base station may communicate according to the measurements determined from the decompression. In some cases, the base station may perform load redistribution based on the measurements.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment may receive a signaling communication that includes information specifying whether a channel state information reference signal (CSI-RS) precoding resource block group includes a same quantity of resource blocks (RBs) that is included in a CSI reporting subband configured for the UE. The CSI-RS precoding resource block group may enable the UE to perform a channel estimation based at least in part on a CSI-RS transmitted by a base station. The UE may perform the channel estimation based at least in part on the CSI-RS transmitted by the base station. The channel estimation may be based at least in part on whether the CSI-RS precoding resource block group includes the same quantity of RBs that is included in the CSI reporting subband configured for the UE. Numerous other aspects are provided.

Abstract: A network entity may transmit a configuration for neural network training parameters for wireless communication by the UE, and the UE may train the neural network at the UE based on the configuration received from the network entity. The network entity may transmit a training command in a wireless message to the UE, and the UE may train the neural network based on the received configuration in response to the received training command. The configuration may include a period of time associated with the training the neural network. The period of time may indicate an action for the UE to perform when the period of time expires, and/or indicate the periodicity of the neural network training.

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 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: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment may determine channel state information for a communication link, wherein the channel state information is based at least in part on a linear combination associated with a plurality of beams of the communication link, wherein a first set of beams used for a first subband, of a plurality of subbands of the communication link, is different than a second set of beams used for a second subband of the plurality of subbands, and wherein the plurality of beams includes the first set of beams and the second set of beams; and transmit the channel state information. Numerous other aspects are provided.

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for reporting configurations for neural network-based processing at a UE. A network entity may transmit to the UE a CSI configuration that includes one or more parameters for a neural network and one or more reference signals. The UE may measure the one or more reference signals based on the CSI configuration. A CSI may be based on the one or more parameters and the measurement of the one or more reference signals. The UE may report the CSI to the network entity based on output of the neural network.