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: 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: 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.

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: Techniques for line-of-sight (LOS) probability map signaling are disclosed. The techniques can include receiving a positioning signal via a wireless link between the UE and a transmission/reception point (TRP) of a radio access network (RAN), identifying an LOS probability function associated with the wireless link based on LOS probability map information provided by a location management function (LMF) of a core network for the RAN, and generating an LOS probability estimate for the wireless link using the LOS probability function.

Abstract: Systems and techniques for wireless communications are described herein. For example, a process for wireless communications at a first network entity include obtaining site-specific data associated with a geographic location and adapting, at the first network entity, a machine learning model based on the site-specific data to generate an updated machine learning model for estimating or predicting of at least one characteristic associated with wireless communications between the first network entity and one or more network entities. The first network entity can experience a trigger event which causes the first network entity to transmit a request for the site-specific data. The triggering event can be based on at least one of a location of the first network entity in the geographic location or the first network entity moving to the geographic location or based on other factors such as a change in a physical characteristic of the location.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a first indication of a first number of antenna ports for which the UE is to report channel state information (CSI), and a second indication of a second number of antenna ports on which the UE is to measure CSI reference signals (CSI-RSs). The second number may be less than the first number. The UE may receive an indication of one or more antenna port parameters, where each may be associated with one of the first number of antenna ports or the second number of antenna ports. The UE may determine the CSI for the first number of antenna ports using the one or more antenna port parameters and measurements made by the UE on the second number of ports, and may transmit a report including the CSI for the first number of ports.

Abstract: In a wireless communication system, a user equipment (UE) may report channel state information (CSI) using a learned dictionary defining a set of sparse vectors. The UE determines a learned dictionary for CSI reporting. For example, the UE receives a shared dictionary from a similar and nearby UE or the UE trains the learned dictionary based on logged CSI measurements. The UE indicates the learned dictionary to a serving base station. The UE measures CSI for a plurality of channels. The UE reports a sparse vector representing the CSI based on the learned dictionary to the serving base station.

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: Disclosed are techniques for training a machine learning model. In an aspect, a user equipment (UE) receives, from a network entity, one or more selection criteria for determining whether the UE is to participate in training the machine learning model, determines whether the UE satisfies the one or more selection criteria during a first period of time, and transmits, to the network entity, after a second period of time, updated parameters for the machine learning model, wherein the machine learning model is updated during the second period of time based on a determination that the UE satisfies the one or more selection criteria.

Abstract: Bi-static radio-based object location detection can include determining, by a wireless device, a location of a remote wireless device; obtaining a ToF and an angle of arrival (AoA) of a reflected WWAN reference signal reflected by a remote object; and determining a location of the remote object based on the location of the remote wireless device, the ToF, and the AoA. In another example, a wireless device includes a wireless transceiver; a non-transitory computer-readable medium; and a processor communicatively coupled to the wireless transceiver and non-transitory computer-readable medium, the processor configured to determine a location of a remote wireless device; obtain a ToF and an angle of arrival (AoA) of a reflected WWAN reference signal reflected by a remote object; and determine a location of the remote object based on the location of the remote wireless device, the ToF, and the AoA.

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: A method of wireless communication by a base station, jointly processes channel information associated with a user equipment, UE, in order to generate a jointly processed report. The channel information is collected from collocated transmit and receive points, TRPs (604, t1, t2, t3), of the base station. The base station transmits (t5) the jointly processed (t4) report to a location server (112).

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: In an aspect, a method of wireless communication performed by a user equipment (UE) includes determining that a positioning model error instance has occurred during a positioning occasion based on 1) a position uncertainty associated with a first position estimate satisfying positioning uncertainty error criteria, wherein the first position estimate is obtained during the positioning occasion by applying a first positioning model to radio frequency fingerprint (RFFP) measurements, 2) a positioning mismatch between the first position estimate of the UE and a second position estimate of the UE satisfying position mismatch error criteria, wherein the second position estimate of the UE is obtained during the positioning occasion by performing a positioning technique that does not use the first positioning model, or 3) any combination thereof; and determining that the first positioning model has failed based on a number of positioning model error instances satisfying positioning model failure criteria.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a user equipment (UE) may obtain a first set of position estimates of the UE based on applying a first positioning model to one or more radio frequency fingerprint positioning (RFFP) measurements. The UE may obtain a second set of position estimates of the UE using a positioning technique that does not use the first positioning model. The UE may transmit a mismatch report, wherein the mismatch report is based on comparing the first set of position estimates with the second set of position estimates.

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: 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.