Abstract:
In order to cancel any interference due to the second signal (e.g., from a non-serving cell) from a signal received at a UE, without receiving additional control information, the UE blindly estimates parameters associated with decoding the second signal. This may include determining a metric based on sets of symbols associated with the signals in order to determine parameters for the second signal, e.g., the transmission mode, modulation format, and/or spatial scheme of the second signal. The parameters for the signal may be determined based on a comparison of the metric with a threshold. When a spatial scheme and a modulation format is unknown, the blind estimation may include determining a plurality of constellations of possible transmitted modulated symbols associated with a potential spatial scheme and modulation format combination. Interference cancellation can be performed using the constellations and a corresponding probability weight.
Abstract:
A method, a computer program product, and an apparatus are provided. The methods and apparatus for wireless communication include receiving a transmission, the transmission including a plurality of resource element groups (REGs). Aspects of the methods and apparatus include selecting a set of REGs from the plurality of REGs, the set of REGs including at least one REG and determining a traffic to pilot ratio (TPR) for the set of REGs based on the transmission and reference signals in the transmission. Aspects of the methods and apparatus include determining whether the set of REGs includes at least one of control information or data based on the TPR and canceling at least one of control information or data from the set of REGs based on the TPR.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a generative channel model (GCM) that outputs channel information pertaining to a digital twin (DT). The UE may receive a configuration of a downlink reference signal. The UE may perform channel estimation using the configuration of the downlink reference signal. The UE may transmit a report, wherein the report includes a precoding indicator, and wherein at least one of computation of the precoding indicator, or a channel estimation algorithm for the channel estimation, uses the GCM. 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 receive, based at least in part on one or more performance metrics associated with an artificial intelligence (AI) model associated with wireless communication, assistance information associated with an expected performance of the AI model. The UE may assess, based at least in part on the assistance information, the expected performance of the AI model. Numerous other aspects are described.
Abstract:
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may communicate, with a network entity, an indication of operation of an artificial intelligence (AI) or (ML) model at the UE and/or the network entity. Based on the indication of the operation of the AI or ML model, the UE may communicate, with the network entity, an indication of the QCL relation between the AI or ML model and reference signal communicated by the UE, a physical channel communicated by the UE, an antenna port of the network entity, or an antenna port of the UE. The QCL relation may indicate the radio characteristics applicable to the AI or ML model. The QCL relation may indicate the radio characteristics applicable to the AI or ML model.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a configuration that includes one or more mapping rules, each of the one or more mapping rules indicating a mapping between one or more reference signal received power (RSRP) measurements, associated with one or more reference signal resources, and one or more indices, associated with one or more feature input vectors for a machine learning model. The UE may initiate a beam prediction based at least in part on the one or more mapping rules. Numerous other aspects are described.
Abstract:
Position determination of a user equipment (UE) is supported using channel measurements obtained for Wireless Access Points (WAPs), wherein the channel measurements are for Line of Sight (LOS) and Non-LOS (NLOS) signals. Based on WAP almanac information and the channel measurements, channel parameters indicative of positions of signal sources relative to a first position of a UE may be determined. Using the first position of the UE and an association of the signal sources with corresponding channel parameters, a second position of the UE may be determined. The position of the UE may be a probability density function. Additionally, position information for signal sources may be determined, such as a probability density function, as well as signal blockage probability and an antenna geometry and the WAP almanac information may be updated accordingly.
Abstract:
Certain aspects of the present disclosure provide techniques for reporting channel state information (CSI). According to certain aspects, a method for wireless communications by a user equipment (UE) generally includes generating channel state information (CSI) comprising a (at least one) fractional rank indication (RI) value for a set of candidate ranks, a first indication of a first layer or first singular vector, and a second indication of a second layer or second singular vector and transmitting the CSI to a network entity.
Abstract:
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. A sequence of data records is accessed, each data record comprising wireless channel measurements and inertial measurement unit (IMU) data. Known position information corresponding to at least a first data record is accessed. A first sequence of positions is determined by processing the sets of IMU data and known position information using a forward operation. A second sequence of positions is determined by processing the sets of IMU data and known position information using a backward operation. An IMU adjustment parameter is generated using the first and second sequences of positions. A pseudo-label is generated for a second data record using the IMU adjustment parameter and the sets of IMU data. A machine learning model is trained, using the second data record and the pseudo-label, to predict positions using one or more wireless channel measurements.
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.