Abstract: A second base station may allocate a set of downlink resources for a downlink transmission. The set of downlink resources may include a subset of null resources, which may serve as a signature for the second base station. The second base station may transmit downlink data or reference signals on the set of downlink resources including the subset of null resources. A first UE may experience the downlink transmission from the second base station as interference. The first UE may identify the set of downlink resources. The first UE may identify the subset of null resources. The first UE may transmit, to a first base station, an indication of the subset of null resources. The first base station may identify the second base station based on the indication of the subset of null resources. The first base station may transmit, to the second base station, an interference coordination message.

Abstract: Certain aspects of the present disclosure provide techniques for configuring a user equipment (UE) with machine learning models based on compute resource limits for the UE. An example method generally includes generating a set of machine learning models for use in a wireless communications device based on one or more compute resource limits associated with a type of the wireless communications device; and deploying the generated set of machine learning models.

Abstract: Aspects described herein relate to encoding, based on an estimated channel matrix of a reference signal received from a base station, channel state information (CSI) using a machine learning (ML)-based CSI encoder, transmitting, to the base station, an output of the ML-based CSI encoder and assistance information related to the estimated channel matrix, and receiving, from the base station, a scheduling grant for a downlink channel having at least one parameter that is based on the output of the ML-based CSI encoder and the assistance information. Other aspects relate to receiving the CSI encoder output and assistance information, and transmitting the scheduling grant.

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: A positioning reference signal measurement method includes: receiving, at a user equipment from a network entity, an OFDM PRS (orthogonal frequency division multiplexing positioning reference signal) comprising a first set of first OFDM symbols that are consecutive and include a first center symbol and at least one pair of first side symbols disposed symmetrically about the first center symbol and having identical resource element sounding patterns; combining the first side symbols in each of the at least one pair of first side symbols to produce at least one first combined symbol; and determining a measurement of the OFDM PRS based on the at least one first combined symbol and the first center symbol.

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: A method of wireless communication by a transmitting device transforms a transmit waveform by an encoder neural network to control power amplifier (PA) operation with respect to non-linearities. The method also transmits the transformed transmit waveform across a propagation channel. A method of wireless communication by a receiving device receives a waveform transformed by an encoder neural network. The method also recovers, with a decoder neural network, the encoder input symbols from the received waveform. A transmitting device for wireless communication calculates distortion error based on a non-distorted digital transmit waveform and a distorted digital transmit waveform. The transmitting device also compresses the distortion error with an encoder neural network of an auto-encoder. The transmitting device transmits to a receiving device the compressed distortion error to compensate for power amplifier (PA) non-linearity.

Abstract: A method of wireless communication by a user equipment (UE), includes estimating a downlink channel to generate a channel estimate. The method also includes obtaining multiple precoding matrices, by a channel state information (CSI) module including a neural network encoder decoder pair, based on the channel estimate and multiple different multiple input multiple output (MIMO) ranks. The method further includes determining a best rank indicator based on the precoding matrices and spectral efficiency estimates for the different MIMO ranks. The method still further includes reporting, to a base station, the best rank indicator, a channel quality index (CQI), and CSI encoder output.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may obtain position information associated with a first subset of network node positions, of a set of network node positions, in connection with a map associated with a wireless coverage area. The network node may obtain map information associated with the map and may determine, based on a machine learning component, a first plurality of predicted pathloss values associated with a second subset of network node positions of the set of network node positions. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may select a machine learning (ML) feature group from among a first ML feature group and a second ML feature group based at least in part on a UE capability of the UE for ML features. The ML features or an ML feature parameter of the first ML feature group may be different than ML features or an ML feature parameter of the second ML feature group. The UE may perform an action associated with wireless communication based at least in part on a model with one or more ML features from the selected ML feature group. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first user equipment (UE) may receive assistance information indicating a second UE to provide assistance relating to training or utilizing a model. The first UE may receive, from the second UE and based at least in part on the assistance information, information based at least in part on a measurement by the second UE. The first UE may train a model using the information based at least in part on the measurement. The first UE may perform a communication based at least in part on the model. Numerous other aspects are described.

Abstract: Generally, the described techniques provide for duplex mode configuration for control information (e.g., duplex mode configuration for communication of downlink control information). For example, duplex modes may be defined (e.g., as full duplex (FD) capable, FD only, half duplex (HD) only, etc.), and such duplex modes may be configured for control resource sets (CORESETs), search space (SS) sets, or both. In some examples, duplex modes configured for a CORESET/SS set may depend on the priority of information conveyed via the CORESET/SS set. For example, a CORESET/SS set for high priority or critical control information may be configured with a HD mode to avoid FD self-interference, while CORESET/SS set for other control information may be configured with a FD mode to improve spectrum efficiency. Techniques for resolving conflicting duplex mode configurations (e.g., when a HD mode is configured for a SS set within a CORESET configured with FD) are also described.

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: Methods, systems, and devices for wireless communications are described. The method may include a wireless device (e.g., a user equipment (UE) or a base station) receiving, from a network node, a machine learning model for use by the wireless device to detect remote interference from a base station. The wireless device may be associated with a first cell and the base station may be associated with a second cell different from the first cell. The wireless device may input one or more parameters into the machine learning model and detect whether the remote interference from the base station is present based on an output of the machine learning model.

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: A base station may set one or more bits of DCI that at least one of indicate or trigger a configuration of an ML model at a UE. The configuration may be based on an association between at least one first ML block for a first procedure and at least one second ML block for a second procedure. The at least one second ML block may be dedicated to a task included in a plurality of tasks associated with the at least one first ML block. The base station may transmit the DCI including the one or more bits to the UE, which may cause the UE to configure the ML model including the association between the at least one first ML block for the first procedure and the at least one second ML block for the second procedure.

Abstract: Certain aspects of the present disclosure provide techniques for semantic communication. A method for wireless communications includes obtaining, by a semantic encoder, a set of real values for transmission to a receiving device; encoding, by the semantic encoder, the set of real values based on a semantic model and a first dimension of a set of dimensions, wherein each different dimension in the set of dimensions corresponds to a different number of real values to output; outputting an encoded set of real values; outputting the encoded set of real values for transmission to the receiving device over a wireless communication channel; obtaining feedback from the receiving device; and using a second dimension of the set of dimensions based on the feedback.

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: Certain aspects of the present disclosure provide techniques for transmitting and processing channel state information (CSI) reference signals (CSI-RS). An exemplary method includes determining a configuration of channel state information reference signals (CSI-RSs), wherein the configuration indicates a set of resource elements (REs) to be used for CSI-RSs and a first mapping of CSI-RS ports to the set of REs; sending an indication of the configuration of the CSI-RSs; and transmitting the CSI-RSs according to the determined configuration.

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.