Abstract:
A method for adjusting a received channel quality indicator is described. A channel quality indicator is received from a wireless communication device. A first transmission time interval for which the channel quality indicator was generated is determined. The received channel quality indicator is adjusted using an outer loop margin. The outer loop margin is dependent on a transmission mode of the first transmission time interval.
Abstract:
A method, apparatus, processing system, and computer program product enable association of mis-aligned subframes (802) (804) from a first and second downlink (1114) with one another, such that a HARQ acknowledgment message (1116) including jointly encoded feedback for the respective subframes can be correctly interpreted by the corresponding cells. Here, an RNC (1108) may provide an RRC message (1110) to the UE (1102) to associate particular subframes with one another. Further, the RNC (1108) may provide NBAP messages (1112) to the cells (1104) (1106) transmitting the downlink signals, so that the cells can associate the HARQ acknowledgment message (1116) with the appropriate subframe. Still further, additional signaling provides for changing the set of associated subframes when needed due to a drift in the timing offset between cells.
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:
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive an indication of one or more encoding operations to use for encoding a compressed dataset, the one or more encoding operations including a differential encoding operation, or an entropy encoding operation, or both. In some examples, using a neural network, the UE may first encode a dataset based on an additional encoding operation to generate a compressed dataset and then quantize the compressed dataset encoded based on the additional encoding operation. Subsequently, after the dataset has been initially encoded and then quantized, the UE may use the indication of the one or more encoding operations to further encode and compress the dataset. The UE may then transmit the dataset to a second device based on the one or more encoding operations.
Abstract:
Methods, systems, and devices for wireless communications are described. Generally, the described techniques at a user equipment (UE) provide for efficiently reporting channel state information (CSI) to a base station with an appropriate level of accuracy. In particular, the base station may indicate a level of accuracy to the UE for reporting CSI. The UE may encode the CSI using a first neural network, and the base station may decode the CSI using a second neural network. The first and second neural networks may form a neural network pair, and the UE may train the neural network pair based on the level of accuracy indicated by the base station. For example, the base station may indicate a loss function corresponding to a level of accuracy with which CSI is to be reported by the UE, and the UE may train the neural network pair using the loss function.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client may select, based at least in part on a classifier, an autoencoder of a set of autoencoders to be used for encoding an observed wireless communication vector to generate a latent vector. The client may transmit the latent vector and an indication of the autoencoder. Numerous other aspects are provided.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first device may transmit a neural network capability indication that indicates a capability of the first device associated with training at least one channel state feedback (CSF) neural network for facilitating providing CSF. The first device may receive, based at least in part on the capability of the first device, a CSF neural network configuration that indicates at least one parameter associated with the at least one CSF neural network. Numerous other aspects are provided.
Abstract:
A network entity may transmit a configuration for neural network training parameters for wireless communication by the UE, and the UE may train the neural network at the UE based on the configuration received from the network entity. The network entity may transmit a training command in a wireless message to the UE, and the UE may train the neural network based on the received configuration in response to the received training command. The configuration may include a period of time associated with the training the neural network. The period of time may indicate an action for the UE to perform when the period of time expires, and/or indicate the periodicity of the neural network training.
Abstract:
A user equipment (UE) receives, from a network entity, a message indicating a change in a set of downlink beams for channel state information reference signals (CSI-RSs), and a context associated with the change. The UE saves state values in an auto-encoder neural network in response to receiving the message and associates the saved state values in the auto-encoder neural network to the context in the received message. The UE also resets the state values in the auto-encoder neural network in response to receiving the message and estimates a channel state based on the CSI-RSs received on the changed set of downlink beams. The UE compresses the channel state with the auto-encoder neural network based on the reset state values and further sends to the network entity, the compressed channel state.
Abstract:
In an aspect, a BS obtains at least one neural network function configured to facilitate a UE to derive a likelihood of at least one set of positioning measurement features being present at a candidate set of positioning estimates for 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 BS transmits the at least one neural network function to the UE. In another aspect, the UE obtains positioning measurement data associated with a location of the UE (e.g., locally the UE, or remotely from the BS). The UE determines a positioning estimate for the UE based at least in part upon the positioning measurement data and the at least one neural network function.