Abstract:
Collision handling of channel state information (CSI) reports is described for enhanced inter-cell interference coordination (eICIC), coordinated multipoint transmission (CoMP), and/or carrier aggregation (CA). Various aspects include prioritization schemes to resolve collisions between different CSI reporting sets in relation to the same component carrier (CC) used with transmission. Multiple stages of prioritization may identify CSI for a report based on various criteria. Tie breaker criteria may be defined for priority among CSI reports that changes for different subframes. In other aspects, PUSCH is utilized to transmit CSI for prioritized reporting sets in a subframe. In yet other aspects, if parallel PUCCH is supported, colliding CSI may be handled on a per PUCCH basis. Other aspects may allow for prioritizing periodic CSI within each of multiple CCs, and then prioritizing over different CCs to handle an interaction of CSI reports for CA, eICIC and/or CoMP.
Abstract:
Aspects presented herein may enable a network entity to configure a group of UEs to simultaneously transmit reference signals and to simultaneously transmit gradient vectors to the network entity, such that the network entity may receive the gradient vectors from the group of UEs as an aggregated gradient vector over the air. In one aspect, a base transmits, to a group of UEs, a configuration that configures the group of UEs to simultaneously transmit one or more group-common reference signals and to simultaneously transmit one or more gradient vectors associated with a federated learning procedure. The network entity receives, from the group of UEs, the one or more group-common reference signals and the one or more gradient vectors based on the configuration via multiple channels. The network entity calculates an average gradient vector based on the one or more group-common reference signals and the one or more gradient vectors.
Abstract:
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may encode channel state feedback (CSF) information to compress the CSF information to a first encoding output associated with a first dimensional space, and apply entropy coding to the first encoding output of the channel state feedback information. The entropy coding may transform the first encoding output to a second encoding output associated with a second dimensional space that is smaller than the first dimensional space of the first encoding output. The UE may transmit a CSF message comprising the second encoding output. A network device may receive the CSF message and apply entropy decoding to the compressed CSF information to partially decompress the compressed CSF information to a first decoding output. The network device may decode the first decoding output to completely decompress the compressed CSF information to a second decoding output.
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:
A method of training an artificial neural network (ANN), receives, from a base station, signal information for a radio frequency signal between the base station and a user equipment (UE). The artificial neural network is trained to determine a location of the UE and to map the environment based on the received signal information and in the absence of labeled data.
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 wireless communications by a user equipment (UE), including receiving, from a network entity, an indication to report at least one of a channel quality indicator (CQI) or a pre-coding matrix indicator (PMI) for one or more future communications resources and sending, to the network entity, a channel state feedback (CSF) report indicating at least one of a predicted CQI or a predicted PMI for the one or more future communications resources prior to the one or more future communications resources occurring in time.
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 receiving, from a base station, a configuration to train a neural network for multiple different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel. The method also includes determining a current SNR of the channel estimate is above a first threshold value. The method further includes training the neural network based on the channel estimate, to obtain a first trained neural network. The method still further includes perturbing the channel estimate to obtain a perturbed channel estimate, and training the neural network based on the perturbed channel estimate, to obtain a second trained neural network. The method includes reporting, to the base station, parameters of the first trained neural network along with the channel estimate, and parameters of the second trained neural network.
Abstract:
Certain aspects of the present disclosure provide techniques for predicting a strength of a transmission beam in a communication system. In one example, the disclosure describes a user equipment (UE) receiving a quality metric from a base station (BS), the quality metric indicating one or more tolerance levels for predicted beam strength as compared to actual beam strength at the UE. In some examples, the UE may transmit, to the BS, one or more indications of actual beam strength as measured at the UE at one or more time periods for one or more transmit beams of the BS.