Abstract: A method of wireless communication by a first network device includes predicting spatial inter-cell downlink interference experienced by a UE. The method also includes communicating with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

Abstract: A method of wireless communication performed by a first transmission device includes determining a set of spectrum sharing parameters based on sensing performed during a sensing period of a current time slot in a fixed contention based spectrum sharing system. The first transmission device shares a spectrum with a second transmission device. The method also includes determining, at a first artificial neural network of the first transmission device, a transmission device action, and/or a transmission parameter in response to receiving the set of spectrum sharing parameters. The method further includes transmitting, from the first transmission device, to a first receiving device during a data transmission phase of the current time slot based on the transmission device action and/or the transmission parameter.

Abstract: Disclosed are techniques for identifying virtual anchors. In an aspect, a network entity obtains a set of data samples associated with a user equipment (UE), each data sample comprising a location of the UE and a set of multipath components (MPCs) obtained by the UE, and determines whether any MPCs in a subset of non-line-of-sight (NLOS) MPCs selected from at least one set of MPCs are associated with any previously identified virtual anchors in a database of found virtual anchors, wherein one or more first NLOS MPCs are removed from the set of data samples based on the one or more first NLOS MPCs being associated with a previously identified virtual anchor, and wherein one or more second NLOS MPCs are added to the database of found virtual anchors based on the one or more second NLOS MPCs not being associated with any previously identified virtual anchor.

Abstract: Various aspects of the present disclosure generally relate to neural network based channel state information (CSI) feedback. In some aspects, a device may obtain a CSI instance for a channel, determine a neural network model including a CSI encoder and a CSI decoder, and train the neural network model based at least in part on encoding the CSI instance into encoded CSI, decoding the encoded CSI into decoded CSI, and computing and minimizing a loss function by comparing the CSI instance and the decoded CSI. The device may obtain one or more encoder weights and one or more decoder weights based at least in part on training the neural network model. Numerous other aspects are provided.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, devices use machine learning (ML) models to support wireless communications. For example, a user equipment (UE) may download ML model information from a network to determine an ML model. The network may additionally configure a status reporting procedure, a fallback procedure, or both for the ML model. In some examples, based on a configuration, the UE may transmit a status report to a base station according to a reporting periodicity, a UE-based trigger, a network-based trigger, or some combination thereof. Additionally or alternatively, the UE may determine to fallback from operating using the ML model to operating in a second mode based on a fallback trigger. In some examples, to restore operating using a downloaded ML model, the UE may download an updated ML model or receive iterative updates to a previously downloaded ML model.

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: 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: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a base station, a federated learning configuration that configures a participation indication to be used by the UE to indicate a participation status of the UE associated with at least one federated learning round corresponding to a machine learning component. The UE may transmit the participation indication to the base station based at least in part on the federated learning configuration. Numerous other aspects are described.

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: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an apparatus may obtain a set of training measurement information associated with a user equipment (UE). The apparatus may obtain a training position value associated with the UE. The apparatus may provide the training position value and the set of training measurement information for training of a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on measurement information. Numerous other aspects are described.

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: Disclosed are techniques for communication. In an aspect, a wireless node receives a configuration associated with one or more reference signal for positioning (RS-P) resources from a position estimation entity. The wireless node performs measurement(s) of a set of RS-P resources, and derives measurement information associated with a first virtual instance of the one or more RS-P resources based on the measurement(s). The wireless node transmits a measurement report comprising the measurement information to the position estimation entity.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) having partially coherent antennas may be configured for simultaneous transmissions on groups of antennas. To achieve the benefits of simultaneous transmissions using groups of antenna that are partially coherent, without having the transmissions affect each other, the UE may apply a hybrid closed-loop multiple-input multiple-output (MIMO) scheme among each antenna in the antenna groups where phase coherence can be maintained. Following the hybrid closed-loop MIMO scheme, the UE may apply a transparent diversity scheme across each antenna of the groups. Alternatively, the UE may first apply the transparent diversity scheme and next apply the hybrid closed-loop MIMO scheme. By applying a hybrid closed-loop MIMO scheme, and a transparent diversity scheme, the UE may fully realize its resources and contribute to an improved spatial diversity for a MIMO system.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive, via a sensor included within the UE, information associated with a base station. For instance, the UE may receive, via a camera included within the UE, an image of the base station. In some cases, the information associated with the base station may also include environment information identifying an antenna of the base station. The UE may then perform, based on the received information, a beam management procedure to track a UE beam corresponding to a base station beam, and communicate with the base station based on the beam management procedure.

Abstract: Various aspects of the present disclosure generally relate to neural network based channel state information (CSI) feedback. In some aspects, a device may obtain a CSI instance for a channel, determine a neural network model including a CSI encoder and a CSI decoder, and train the neural network model based at least in part on encoding the CSI instance into encoded CSI, decoding the encoded CSI into decoded CSI, and computing and minimizing a loss function by comparing the CSI instance and the decoded CSI. The device may obtain one or more encoder weights and one or more decoder weights based at least in part on training the neural network model. Numerous other aspects are provided.

Abstract: Aspects presented herein may enhance the accuracy and/or latency of UE positioning based on crowd-sourcing, where a network entity may compute a position estimate of a UE based on neighbor-cell scan data from the UE and one or more reference UEs. In one aspect, a network entity receives a first set of measurements associated with at least one cell from a UE. The network entity performs a position estimation of the UE based on at least one of the first set of measurements associated with the at least one cell, a second set of measurements for each of a set of reference UEs, or a location of each of the set of reference UEs via an ML model, where the UE and the set of reference UEs include at least one common cell.

Abstract: An apparatus for wireless communication at a UE is provided. The apparatus is configured to receive a configuration to report interference measurement information indicating interference measurements for each interference measurement resource of a set of interference measurement resources and Rx beam information used by the UE for performing interference measurements on each interference measurement resource of the set of interference measurement resources. The apparatus is configured to receive a set of interference measurement reference signals on the set of interference measurement resources, and to measure interference on each interference measurement resource of the set of interference measurement resources to obtain the interference measurement information. Each interference measurement is through one Rx beam of a set of Rx beams.

Abstract: Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning. The UE may transmit, to a base station, a request message that includes an indication of a machine learning model or a neural network function based at least in part on a trigger event. In response to the request message, the base station may transmit a machine learning model, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function and may transmit an activation message to the UE to implement the machine learning model and the neural network function.

Abstract: Disclosed are techniques for training a position estimation module. In an aspect, a first network entity obtains a plurality of positioning measurements, obtains a plurality of positions of one or more user equipments (UEs), the plurality of positions determined based on the plurality of positioning measurements, stores the plurality of positioning measurements as a plurality of features and the plurality of positions as a plurality of labels corresponding to the plurality of features, and trains the position estimation module with the plurality of features and the plurality of labels to determine a position of a UE from positioning measurements taken by the UE.

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.