Compression and reconstruction of interference distributions

Abstract

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may transmit compressed interference information to a network entity. The UE may measure interference at the UE across a set of interference measurement resources. The UE may determine an interference distribution across the set of resources. The UE may encode the interference distribution using a compression scheme that may reduce a payload or size of the interference distribution. The compression scheme may include compressing the distribution of interference. Encoding the interference information may include generating a mean vector and a covariance matrix that represent the distribution of measured interference across a given set of interference measurement resources. The UE may transmit the encoded interference information, and a network entity may decode the encoded interference information. The network entity may schedule communication with the UE based on the interference information.

Description

[Technical field]

[0001] cross reference

[0001] This patent application claims the benefit of U.S. Provisional Patent Application No. 63 / 305,174, entitled "INTERFERENCE DISTRIBUTION COMPRESSION AND RECONSTRUCTION," filed on January 31, 2022, to MARZBAN et al., and U.S. Patent Application No. 17 / 876,397, entitled "INTERFERENCE DISTRIBUTION COMPRESSION AND RECONSTRUCTION," filed on July 28, 2022, to MARZBAN et al., each of which is assigned to the assignee of the present application and expressly incorporated by reference herein.

[0002] introduction The following relates generally to wireless communications, and more specifically to interference suppression and reporting.

[0003]

[0003] Wireless communication systems are widely deployed to provide various types of communication content, such as voice, video, packet data, messaging, broadcasts, and the like. These systems may be capable of supporting communication with multiple users by sharing available system resources (e.g., time, frequency, and power). Examples of such multiple access systems include fourth generation (4G) systems, such as Long Term Evolution (LTE) systems, LTE-Advanced (LTE-A) systems, or LTE-A Pro systems, and fifth generation (5G) systems, which may be referred to as New Radio (NR) systems. These systems may employ technologies such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM), etc. A wireless multiple-access communication system may include one or more base stations or one or more network access nodes, each simultaneously supporting communication for multiple communication devices, which may otherwise be known as user equipment (UE). Summary of the Invention

[0004] A method for wireless communication in a user equipment (UE) is described. In some examples, the method may include measuring interference at the UE across a set of interference measurement resources. In some examples, the method may include encoding interference information representing a distribution of interference at the UE based on the measured interference across the set of interference measurement resources according to a compression scheme. In some examples, the method may further include transmitting the interference information encoded according to the compression scheme to a first network entity.

[0005]

[0005] An apparatus for wireless communication in a UE is described. The apparatus may include a processor and a memory coupled to the processor. In some examples, the processor may be configured to cause the apparatus to measure interference at the UE across a set of interference measurement resources. In some examples, the processor may be configured to encode interference information representing a distribution of interference at the UE based on the measured interference across the set of interference measurement resources according to a compression scheme. In some examples, the processor may be configured to transmit the interference information encoded according to the compression scheme to a first network entity.

[0006] Another apparatus for wireless communication in a UE is described. In some examples, the apparatus may include means for measuring interference at the UE across a set of interference measurement resources. In some examples, the apparatus may include means for encoding interference information representing a distribution of interference at the UE based on the measured interference across the set of interference measurement resources according to a compression scheme. In some examples, the apparatus may include means for transmitting the interference information encoded according to the compression scheme to a first network entity.

[0007]

[0007] A non-transitory computer-readable medium storing code for wireless communication at a UE is described. In some examples, the code may include instructions executable by a processor to measure interference at the UE across a set of interference measurement resources. In some examples, the code may include instructions executable by a processor to encode interference information representing a distribution of interference at the UE based on the measured interference across the set of interference measurement resources according to a compression scheme. In some examples, the code may include instructions executable by a processor to transmit the interference information encoded according to the compression scheme to a first network entity.

[0008]

[0008] In some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification, the interference at the UE may be interference plus noise, and the distribution of interference may be a distribution of interference plus noise across a set of interference measurement resources.

[0009]

[0009] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the distribution of interference at the UE includes a probability mass function for a set of resources in time, frequency, and / or space.

[0010]

[0010] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the set of resources in time, frequency, and / or space includes a set of interference measurement resources.

[0011]

[0011] In some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification, the set of resources in time, frequency, and / or space includes resources that precede the set of interference measurement resources, and the distribution of interference at the UE is based on the measured interference across the set of interference measurement resources.

[0012]

[0012] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the set of resources in time, frequency, and / or space includes resources that are later than the set of interference measurement resources, and a distribution of interference at the UE is predicted based at least in part on measured interference across the set of interference measurement resources.

[0013]

[0013] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for generating a condensed, estimated or predicted interference distribution across a set of interference measurement resources.

[0014]

[0014] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the compression scheme includes a codeword-based compression scheme or an artificial neural network-based compression scheme.

[0015]

[0015] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for generating a mean vector and a covariance matrix of latent random variables representing the distribution of interference at the UE across a set of interference measurement resources, the set of interference measurement resources including two or more sets of time resources, frequency resources, or spatial resources.

[0016]

[0016] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include an operation, feature, means, or instruction for receiving an indication of an encoding configuration for encoding interference information from the first network entity or one or more second network entities associated with the first network entity.

[0017]

[0017] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, means, or instructions for transmitting an indication of the UE's capability to encode interference information to the first network entity or one or more second network entities associated with the first network entity, where receiving the indication of the encoding configuration for encoding the interference information includes receiving an indication of an index associated with the encoding configuration in response to transmitting the indication of the UE's capability to encode interference information from the first network entity or one or more second network entities associated with the first network entity, and a set of encoding configurations including the encoding configuration may be associated with a set of indexes including the index.

[0018]

[0018] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the encoding configuration includes configuration for one of an autoencoder or an artificial neural network.

[0019]

[0019] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, means, or instructions for selecting one coding configuration from a set of coding configurations for encoding interference information, where each coding configuration in the set of coding configurations may be associated with a respective index in a set of indexes, and for sending an indication of one index in the set of indexes associated with the selected coding configuration to the first network entity or one or more second network entities associated with the first network entity.

[0020]

[0020] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include an operation, feature, means, or instruction for receiving an indication of one or more parameters associated with the compression scheme from the first network entity or one or more second network entities associated with the first network entity.

[0021]

[0021] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, a loss function, or a combination thereof.

[0022]

[0022] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include an operation, feature, means, or instruction for receiving an indication of the set of interference measurement resources from the first network entity or one or more second network entities associated with the first network entity.

[0023]

[0023] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include an operation, feature, means, or instruction for receiving an indication of one or more parameters associated with measuring interference at the UE across the set of interference measurement resources from the first network entity or one or more second network entities associated with the first network entity.

[0024]

[0024] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, one or more parameters associated with measuring interference at a UE across a set of interference measurement resources include frequency granularity, time granularity, spatial granularity, or a combination thereof.

[0025]

[0025] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include an operation, feature, means, or instruction for receiving from the first network entity or one or more second network entities associated with the first network entity an indication of an input format for encoding interference information representing a distribution of interference at the UE.

[0026]

[0026] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for transmitting a channel condition feedback report including interference information encoded according to a compression scheme.

[0027]

[0027] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for determining one or more model parameters associated with the compression scheme using an artificial neural network associated with the compression scheme, and for transmitting the one or more model parameters to the first network entity or one or more second network entities associated with the first network entity.

[0028]

[0028] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include an operation, feature, means, or instruction for receiving one or more parameters associated with the compression scheme based on transmitting one or more model parameters from the first network entity or one or more second network entities associated with the first network entity.

[0029]

[0029] A method of wireless communication in a network entity is described. In some examples, the method may include obtaining encoded interference information representing a distribution of interference. In some examples, the method may include decoding the encoded interference information according to a compression scheme to output decoded interference information.

[0030]

[0030] An apparatus for wireless communication in a network entity is described. The apparatus may include a processor and a memory coupled to the processor. In some examples, the processor may be configured to cause the apparatus to obtain encoded interference information representing a distribution of interference. In some examples, the processor may be configured to cause the apparatus to decode the encoded interference information according to a compression scheme to output the decoded interference information.

[0031] Another apparatus for wireless communication in a network entity is described. In some examples, the apparatus may include means for obtaining encoded interference information representative of a distribution of interference. In some examples, the apparatus may include means for decoding the encoded interference information according to a compression scheme to output the decoded interference information.

[0032]

[0032] A non-transitory computer-readable medium storing code for wireless communication in a network entity is described. In some examples, the code may include instructions executable by a processor to obtain encoded interference information representative of a distribution of interference. In some examples, the code may include instructions executable by a processor to decode the encoded interference information according to a compression scheme to output the decoded interference information.

[0033]

[0033] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the distribution of interference may be a distribution of interference plus noise across a set of interference measurement resources.

[0034] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the coded interference information includes a mean vector and a covariance matrix of latent random variables that represent a distribution of interference at the UE across the set of interference measurement resources. The methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, means, or instructions for generating samples based on the mean vector and the covariance matrix described, and for decoding the coded interference information based at least in part on the samples.

[0035]

[0035] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for outputting scheduling information for communication at the UE based on the decoded interference information.

[0036]

[0036] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include an operation, function, means, or instruction for outputting an indication of an encoding configuration for encoding interference information in the UE.

[0037]

[0037] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, functions, means, or instructions for obtaining an indication of a capability of the UE to encode interference information, and for outputting an indication of an index associated with the encoding configuration based on the indication of the capability of the UE to encode interference information, and a set of encoding configurations including the encoding configuration may be associated with a set of indexes including the index.

[0038]

[0038] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the encoding configuration includes configuration for one of an autoencoder or an artificial neural network.

[0039]

[0039] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for obtaining an indication of one index of a set of indexes associated with a selected encoding configuration, and each encoding configuration of the set of encoding configurations may be associated with a respective index of the set of indexes.

[0040]

[0040] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for outputting an indication of one or more parameters associated with the compression scheme.

[0041]

[0041] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, a loss function, or a combination thereof.

[0042]

[0042] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for outputting an indication of one or more parameters associated with measuring interference across a set of interference measurement resources.

[0043]

[0043] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, one or more parameters associated with measuring interference across a set of interference measurement resources include frequency granularity, time granularity, spatial granularity, or a combination thereof.

[0044]

[0044] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for outputting instructions in an input format for encoding interference information representing a distribution of interference.

[0045]

[0045] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for obtaining a channel condition feedback report including encoded interference information encoded according to a compression scheme.

[0046]

[0046] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for determining one or more model parameters associated with the compression scheme using an artificial neural network associated with the compression scheme, and for outputting one or more parameters associated with the compression scheme based on the one or more model parameters.

[0047]

[0047] Some examples of the methods, apparatus, and non-transitory computer-readable media described in this specification may further include operations, features, means, or instructions for obtaining one or more model parameters associated with the compression scheme, and for outputting one or more parameters associated with the compression scheme based on the one or more model parameters. [Brief description of the drawings]

[0048] [Figure 1] Illustrates an example of a wireless communication system that supports compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. [Diagram 2]

[0049] 1 illustrates an example of a wireless communication system that supports compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. [Figure 3a]

[0050] 1 illustrates an example encoding and decoding scheme that supports compression and reconstruction of interference distributions, in accordance with one or more aspects of the present disclosure. [Figure 3b] 1 illustrates an example encoding and decoding scheme that supports compression and reconstruction of interference distributions, in accordance with one or more aspects of the present disclosure. [Figure 4]

[0051] 1 illustrates an example of an autoencoder that supports compression and reconstruction of interference distributions, in accordance with one or more aspects of the present disclosure. [Diagram 5]

[0052] 1 illustrates an example of a machine learning process that supports compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. [Figure 6]

[0053] 1 illustrates an example of a process flow that supports compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. [Figure 7]

[0054] FIG. 1 illustrates a block diagram of a device that supports compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. [Figure 8] FIG. 1 illustrates a block diagram of a device that supports compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. [Figure 9]

[0055] 1 illustrates a block diagram of a communications manager that supports compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. [Figure 10]

[0056] FIG. 1 illustrates a diagram of a system including a device that supports compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. [Figure 11]

[0057] FIG. 1 illustrates a block diagram of a device that supports compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. [Figure 12] FIG. 1 illustrates a block diagram of a device that supports compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. [Figure 13]

[0058] 1 illustrates a block diagram of a communications manager that supports compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. [Figure 14]

[0059] FIG. 1 illustrates a diagram of a system including a device that supports compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. [Figure 15]

[0060] 1 shows a flowchart illustrating a method for supporting compression and reconstruction of interference distributions, in accordance with one or more aspects of the present disclosure. [Figure 16] 1 shows a flowchart illustrating a method for supporting compression and reconstruction of interference distributions, in accordance with one or more aspects of the present disclosure. [Figure 17] 1 shows a flowchart illustrating a method for supporting compression and reconstruction of interference distributions, in accordance with one or more aspects of the present disclosure. [Figure 18] 1 shows a flowchart illustrating a method for supporting compression and reconstruction of interference distributions, in accordance with one or more aspects of the present disclosure. [Figure 19] 1 shows a flowchart illustrating a method for supporting compression and reconstruction of interference distributions, in accordance with one or more aspects of the present disclosure. [Figure 20] 1 shows a flowchart illustrating a method for supporting compression and reconstruction of interference distributions, in accordance with one or more aspects of the present disclosure. [Figure 21]

[0061] 1 illustrates an example of a network architecture that supports compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0049]

[0062] Interference at a UE may be caused, for example, by communications at a neighboring base station or by sidelink communications between other UEs. The interference (or interference plus noise) experienced at a UE may vary in the time, frequency, and spatial domains. The interference plus noise experienced at a UE may refer to the interference power plus noise power observed at the UE. The interference (or interference plus noise) experienced at a UE may also have correlations in the time, frequency, and spatial domains. For example, a change in the interference (or interference plus noise) in the time domain at a UE may affect the interference (or interference plus noise) in the frequency or spatial domain. The UE may predict interference (or interference plus noise) on future communication resources based on the measured interference (or interference plus noise) across communication resources in the past. For example, the UE may determine a correlation of past interference (or interference plus noise) measurements in the time, frequency, and spatial domains and predict future interference (or interference plus noise) based on the determined correlation.

[0050]

[0063] A UE in a wireless communication system may measure the interference (or interference plus noise) at the UE using interference measurement resources such as a Channel State Information (CSI) Reference Signal (CSI-RS), CSI Interference Measurement (CSI-IM), or generally through an Interference Measurement Resource (IMR). CSI-RS refers to a reference signal transmitted by a serving base station or a network node that the UE may use to estimate the channel and report channel quality information to the serving base station or network node. CSI-IM refers to a set of resource elements reserved for interference measurement, which may be configurable, for example, via radio resource control. IMR may be a time-frequency resource assigned to the UE by the network for the UE to measure the interference at the UE. The interference (or interference plus noise) at the UE may be reported to the serving base station or network node via a Channel State Feedback (CSF) report. The CSF report may not report information about the time correlation characteristics, frequency correlation characteristics, or spatial correlation characteristics of the interference (or interference plus noise). Thus, a serving base station or network node receiving the CSF report is not informed about time, frequency, or spatial correlation characteristics of the interference (or interference plus noise) at the UE. Thus, the serving base station or network node may not be able to use these interference (or interference plus noise) correlation characteristics when scheduling communication for the UE based on the CSF report. Thus, the serving base station or network node may schedule communication for the UE using resources with relatively high interference (or interference plus noise), which may result in signal loss or inefficient communication. For example, a communication may have relatively high interference if the interference results in data signal loss or inefficiency of data communication. Furthermore, because the interference (or interference plus noise) at the UE may have large variations in the time, frequency, or spatial domains, explicit reporting of the interference at the UE to the serving base station or network node may be associated with large resource overhead.

[0051]

[0064] To enable the base station or network node to take into account time, frequency, or spatial correlation characteristics of the interference (or interference plus noise) at the UE, the UE may report interference (or interference plus noise) information to the base station or network node. The reported interference (or interference plus noise) information may represent a distribution of interference (or interference plus noise) at the UE. The interference (or interference plus noise) distribution or interference (or interference plus noise) distribution may be a sequence of estimated and / or predicted interference (or interference plus noise) values ​​based on multiple instances of measured interference (or interference plus noise) measurements across a set of multiple interference measurement resources in the time domain, frequency domain, and / or spatial domain at the UE. The estimated interference (or interference plus noise) value may refer to a past interference (or interference plus noise) estimated based on multiple instances of measured interference (or interference plus noise) measurements across a set of multiple interference measurement resources (e.g., since the measurement value itself may be an estimate). A predicted interference (or interference plus noise) value may refer to a future interference (or interference plus noise) value predicted based on multiple instances of measured interference (or interference plus noise) measurements across a set of multiple interference measurement resources. For example, in an aspect, the UE may transmit to the base station interference (or interference plus noise) information encoded (e.g., compressed) according to a compression scheme to reduce resource overhead associated with explicit interference (or interference plus noise) distribution reporting while allowing the base station to consider interference (or interference plus noise) correlation when scheduling communications at the UE. The UE may measure multiple instances of interference (or interference plus noise) at the UE across a set of multiple interference measurement resources in the time domain, frequency domain, and / or spatial domain. The set of interference measurement resources may include, for example, CSI-RS, CSI-IM, or IMR. The UE may determine an interference (or interference plus noise) distribution across the set of resources. The UE may encode the interference (or interference plus noise) distribution using a compression scheme that may reduce the payload or size of the interference (or interference plus noise) distribution.The compression scheme may involve compressing the distribution of the measured interference (or interference plus noise) over a given set of interference measurement resources.

[0052]

[0065] According to one or more examples, the interference (or interference plus noise) distribution can be a probability density function or a probability mass function for the time, frequency, and spatial variables. A probability density function indicates the probability of a value of a continuous random variable falling within a certain range. The probability that a random variable has a value within the range x and x+dx is f(x)dx. A probability mass function is a function that gives the exact probability that a discrete random variable takes a certain value. For example, if X is in the range R X ={x1,x2,x3...}, then we have a function P X (x k )=P(X=x k ) (where k=1,2,3,...) is the probability mass function of X.

[0053]

[0066] In some examples, encoding the interference (or interference plus noise) information according to a compression scheme may include generating a mean vector and a covariance matrix of latent random variables that represent the distribution of the measured interference (or interference plus noise) over a given set of interference measurement resources. The term "latent" in latent value, latent variable, or latent vector refers to a value, variable, or vector that is derived or inferred through a mathematical model, and not a value, variable, or vector that is directly measured or observed. The mean vector and covariance matrix may indicate the distribution of a data matrix or sequence (e.g., a stochastic latent random vector) associated with the measured interference (or interference plus noise). For example, if the data matrix consists of a set of measurements of time, frequency, and spatial variables of the interference (or interference plus noise), the mean vector may be a vector of the average of the set of measurements for each time, frequency, and spatial variable of the interference (or interference plus noise). The covariance matrix may include the variances of the time, frequency, and spatial variables along the main diagonal of the covariance matrix, and the covariances between each pair of variables at the other covariance matrix positions. The UE may transmit coded interference (or interference plus noise) information, and the base station may receive and decode the coded interference (or interference plus noise) information. The base station or network node may schedule communication with the UE based on the interference (or interference plus noise) information.

[0054]

[0067] In some examples, the UE may receive control signaling to configure aspects of interference measurement resources and coding. For example, the base station may configure interference measurement resources for the UE, a compression scheme, parameters of the compression scheme, an encoder (e.g., a coding configuration for the encoder), an input format to the encoder, and / or parameters associated with measuring interference (or interference plus noise) at the UE. Exemplary coding configurations may include, for example, coding configurations for an autoencoder or an artificial neural network. For example, the base station may configure the granularity of the interference (or interference plus noise) estimation. Granularity refers to a scale size or measurement step size. For example, the base station may configure, for frequency granularity, whether the interference (or interference plus noise) estimation is based on full band or sub-band (and different granularity for different sub-bands). As another example, the base station may configure, for time granularity, whether the interference (or interference plus noise) estimation is based on symbol level interference (or interference plus noise), slot level interference (or interference plus noise), or multi-slot level interference (or interference plus noise). As another example, for spatial granularity, the base station may configure an interference (or interference plus noise) estimate for a particular beam. As another example, the input format to the encoder may include an estimated interference (or interference plus noise) distribution, a zero-power or non-zero-power CSI-RS for interference (or interference plus noise) measurement, or an estimated interference (or interference plus noise) on a previous resource. The CSI-RS used to measure the channel may be referred to as a non-zero-power CSI-RS. Zero-power CSI-RS refers to a CSI-RS that occupies a configured resource element but in which the base station does not transmit any energy in the resource element.

[0055]

[0068] The UE may include an artificial neural network (NN) that may update model parameters associated with the compression scheme based on past interference (or interference plus noise) measurements at the UE. Artificial neural networks that may support such machine learning techniques for channel compression include fully connected NNs, batch normalized NNs, dropout NNs, convolutional NNs, residual NNs, rectified linear unit (ReLU) NNs, and other types of NNs. A fully connected NN includes a series of fully connected layers that connect all neurons in one layer to all neurons in other layers. A batch normalized NN includes a normalization step that fixes the mean and variance of each layer of the input of the neural network. A dropout NN may ignore randomly selected nodes during training of the neural network. For example, at each training stage, individual nodes are dropped from the dropout network with probability 1-p or are kept with probability p. A convolutional NN is also referred to as a shift-invariant or space-invariant artificial neural network (SIANN). A convolutional neural network includes an input layer, a hidden layer, and an output layer. In feedforward neural networks, intermediate layers may be referred to as hidden because their inputs and outputs are masked by an activation function and a final convolution. In convolutional neural networks, hidden layers include layers that perform convolutions. Hidden layers may include layers that perform a dot product of a convolution kernel with the input matrix of the layer. This product may be a Frobenius dot product, and its activation function is typically ReLU. As the convolution kernel slides along the input matrix of the layer, the convolution operation produces a feature map, which contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers. ReLU NNs are based on their argument f(x)=x += max(0,x), where x is the input to the neuron. The residual NN may utilize skip connections to jump layers of the NN and may be implemented with a two-layer or three-layer skip including nonlinearity (e.g., ReLus) and batch normalization between layers. The UE may report the trained or updated model parameters to the base station. The base station may receive the trained or updated model parameters from multiple UEs. The base station may update parameters associated with encoding or decoding based on the trained parameters received from multiple UEs.

[0056]

[0069] In some examples, the compression scheme may include an autoencoder. An autoencoder may refer to a NN that uses a feed-forward approach to reconstruct an output from an input. The input is compressed in an encoder and then sent to a decoder for decompression. The autoencoder may be trained to minimize loss in the output.

[0057]

[0070] Encoding the interference (or interference plus noise) information according to a compression scheme may enable the UE to report measured and / or predicted interference (or interference plus noise) at the UE without transmitting the entire measured and / or predicted interference (or interference plus noise) distribution, thereby reducing resource overhead, and may enable a serving base station or network node to receive and determine correlation characteristics of the interference (or interference plus noise) at the UE that the base station may consider when scheduling communications for the UE. For example, the base station may avoid scheduling communications for the UE on communications resources with high estimated or predicted interference (or interference plus noise). The base station may schedule communications for the UE on communications resources with low estimated or predicted interference. Furthermore, using machine learning techniques at the UE and / or base station may enable the UE and base station to minimize errors in compression and reconstruction of the interference (or interference plus noise) information, while also reducing resource overhead associated with reporting interference (or interference plus noise) information.

[0058]

[0071] Aspects of the present disclosure are first described in the context of a wireless communication system. Aspects of the present disclosure are further illustrated by and described with reference to encoding and decoding schemes, machine learning processes, and process flows. Aspects of the present disclosure are further illustrated by and described with reference to apparatus diagrams, system diagrams, and flow charts related to compression and reconstruction of interference distributions.

[0059]

[0072] 1 illustrates an example of a wireless communication system 100 supporting compression and reconstruction of interference distribution in accordance with one or more aspects of the present disclosure. The wireless communication system 100 may include one or more base stations 105, one or more UEs 115, and a core network 130. In some examples, the wireless communication system 100 may be a Long Term Evolution (LTE) network, a LTE-Advanced (LTE-A) network, a LTE-A Pro network, or a New Radio (NR) network. In some examples, the wireless communication system 100 may support enhanced broadband communications, ultra-reliable communications, low latency communications, communications with low-cost and low-complexity devices, or any combination thereof.

[0060]

[0073] The base stations 105 may be distributed throughout a geographic area to form the wireless communication system 100 and may be devices of different forms or with different capabilities. The base stations 105 and the UEs 115 may communicate wirelessly via one or more communication links 125. Each base station 105 may provide a coverage area 110 over which the UEs 115 and the base stations 105 may establish one or more communication links 125. The coverage area 110 may be an example of a geographic area over which the base stations 105 and the UEs 115 may support communication of signals according to one or more radio access technologies.

[0061]

[0074] The UEs 115 may be distributed throughout the coverage area 110 of the wireless communication system 100, and each UE 115 may be fixed, or mobile, or both, at different times. The UEs 115 may be devices of different forms or with different capabilities. Some example UEs 115 are shown in FIG. 1. The UEs 115 described herein may be capable of communicating with various types of devices, such as other UEs 115, base stations 105, or network equipment (e.g., core network nodes, relay devices, integrated access and backhaul (IAB) nodes, or other network equipment) as shown in FIG. 1.

[0062]

[0075] In some examples, one or more components of the wireless communication system 100 may operate as or be referred to as a network node. As used herein, a network node may refer to any UE 115, base station 105, core network 130 entity, apparatus, device, or computing system configured to perform any of the techniques described herein. For example, a network node may be a UE 115. As another example, a network node may be a base station 105. As another example, a first network node may be configured to communicate with a second network node or a third network node. In one aspect of this example, the first network node may be a UE 115, the second network node may be a base station 105, and the third network node may be a UE 115. In another aspect of this example, the first network node may be a UE 115, the second network node may be a base station 105, and the third network node may be a base station 105. In yet other aspects of this example, the first, second, and third network nodes may be different. Similarly, reference to a UE 115, a base station 105, an apparatus, a device, or a computing system may include disclosure of the UE 115, the base station 105, the apparatus, the device, or the computing system that is a network node. For example, a disclosure that the UE 115 is configured to receive information from the base station 105 also discloses that the first network node is configured to receive information from the second network node. In this example, consistent with the present disclosure, the first network node may refer to the first UE 115, the first base station 105, the first apparatus, the first device, or the first computing system configured to receive information, and the second network node may refer to the second UE 115, the second base station 105, the second apparatus, the second device, or the second computing system.

[0063]

[0076] The base stations 105 may communicate with the core network 130, or with each other, or both. For example, the base stations 105 may interface with the core network 130 through one or more backhaul links 120 (e.g., via an S1, N2, N3, or other interface). The base stations 105 may communicate with each other via the backhaul links 120 (e.g., via an X2, Xn, or other interface), either directly (e.g., between the base stations 105) or indirectly (e.g., via the core network 130), or both. In some examples, the backhaul links 120 may be or include one or more wireless links. The UE 115 may communicate with the core network 130 through a communication link 155.

[0064]

[0077] One or more of the base stations 105 described herein may include or be referred to as a base transceiver station, wireless base station, access point, wireless transceiver, NodeB, eNodeB (eNB), next generation NodeB or giga-NodeB (any of which may be referred to as gNB), Home NodeB, Home eNodeB, or other suitable terminology by one of ordinary skill in the art.

[0065]

[0078] The UE 115 may include or be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or any other suitable terminology, and a "device" may also be referred to as a unit, a station, a terminal, or a client, among other examples. The UE 115 may also include or be referred to as a personal electronic device, such as a cellular phone, a personal digital assistant (PDA), a tablet computer, a laptop computer, or a personal computer. In some examples, the UE 115 may include or be referred to as a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine type communications (MTC) device, among other examples, which may be implemented in various items, such as an appliance, or a vehicle, a meter, among other examples.

[0066]

[0079] The UEs 115 described herein may be capable of communicating with various types of devices, such as other UEs 115, which may act as relays, as shown in FIG. 1, as well as base stations 105 and network equipment, including macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations, among other examples.

[0067]

[0080] The UE 115 and the base station 105 may wirelessly communicate with each other via one or more communication links 125 on one or more carriers. The term “carrier” may refer to a set of radio frequency spectrum resources having a defined physical layer structure for supporting the communication links 125. For example, a carrier used for the communication links 125 may include a portion (e.g., a bandwidth part (BWP)) of a radio frequency spectrum band that operates according to one or more physical layer channels for a given radio access technology (e.g., LTE, LTE-A, LTE-A Pro, NR). Each physical layer channel may carry collection signaling (e.g., synchronization signals, system information), control signaling to coordinate operation on the carrier, user data, or other signaling. The wireless communication system 100 may support communication with the UE 115 using carrier aggregation or multi-carrier operation. The UE 115 may be configured with multiple downlink component carriers and one or more uplink component carriers according to a carrier aggregation configuration. Carrier aggregation may be used with both frequency division duplexing (FDD) and time division duplexing (TDD) component carriers.

[0068]

[0081] In some examples (e.g., in a carrier aggregation configuration), a carrier may also have collection or control signaling to coordinate operation with respect to other carriers. A carrier may be associated with a frequency channel (e.g., an evolved universal mobile telecommunication system terrestrial radio access (E-UTRA) absolute radio frequency channel number (EARFCN)) and may be arranged according to a channel raster for discovery by the UE 115. A carrier may operate in a standalone mode, where initial collection and connection may be made by the UE 115 over the carrier, or the carrier may operate in a non-standalone mode, where a connection is anchored using a different carrier (e.g., of the same or different radio access technology).

[0069]

[0082] The communication links 125 shown in the wireless communication system 100 may include uplink transmissions from the UE 115 to the base station 105 or downlink transmissions from the base station 105 to the UE 115. A carrier may carry downlink or uplink communications (e.g., in FDD mode) or may be configured to carry downlink and uplink communications (e.g., in TDD mode).

[0070]

[0083] A carrier may be associated with a particular bandwidth of the radio frequency spectrum, and in some examples, the carrier bandwidth may be referred to as the carrier or the “system bandwidth” of the wireless communication system 100. For example, the carrier bandwidth may be one of several determined bandwidths for a particular radio access technology carrier (e.g., 1.4, 3, 5, 10, 15, 20, 40, or 80 Megahertz (MHz)). A device of the wireless communication system 100 (e.g., a base station 105, a UE 115, or both) may have a hardware configuration that supports communication over a particular carrier bandwidth or may be configurable to support communication over one of a set of carrier bandwidths. In some examples, the wireless communication system 100 may include a base station 105 or a UE 115 that supports simultaneous communication over carriers associated with multiple carrier bandwidths. In some examples, each served UE 115 may be configured to operate over a portion (e.g., a sub-band, BWP) or all of the carrier bandwidth.

[0071]

[0084] A signal waveform transmitted on a carrier may be composed of multiple subcarriers (e.g., using a multi-carrier modulation (MCM) technique such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform spread OFDM (DFT-S-OFDM)). In a system employing MCM techniques, a resource element may consist of one symbol period (e.g., the duration of one modulation symbol) and one subcarrier, where the symbol period and the subcarrier spacing are inversely related. The number of bits carried by each resource element may depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both). Thus, the more resource elements the UE 115 receives and the higher the order of the modulation scheme, the higher the data rate may be for the UE 115. Wireless communication resources may refer to a combination of radio frequency spectrum resources, time resources, and spatial resources (e.g., spatial layers or beams), and the use of multiple spatial layers may further increase data rates or data integrity for communications with UE 115.

[0072]

[0085] One or more numerologies for a carrier may be supported, and the numerology may include a subcarrier spacing (Δf) and a cyclic prefix. A carrier may be divided into one or more BWPs having the same or different numerologies. In some examples, a UE 115 may be configured with multiple BWPs. In some examples, a single BWP for a carrier may be active at a given time, and communication for the UE 115 may be limited to one or more active BWPs.

[0073]

[0086] The time interval for the base station 105 or the UE 115 is, for example, T s =1 / (Δf max N f ) seconds, where Δfmax may represent the maximum supported subcarrier spacing, and N f may represent the maximum discrete Fourier transform (DFT) size supported. The communication resource time intervals may be organized according to radio frames, each having a specified duration (e.g., 10 milliseconds (ms)). Each radio frame may be identified by a system frame number (SFN) (e.g., ranging from 0 to 1023).

[0074]

[0087] Each frame may include multiple consecutively numbered subframes or slots, and each subframe or slot may have the same duration. In some examples, a frame may be divided (e.g., in the time domain) into subframes, and each subframe may be further divided into several slots. Alternatively, each frame may include a variable number of slots, and the number of slots may depend on the subcarrier spacing. Each slot may include several symbol periods (e.g., depending on the length of a cyclic prefix prepended to each symbol period). In some wireless communications systems 100, a slot may be further divided into multiple minislots containing one or more symbols. Excluding the cyclic prefix, each symbol period may include one or more (e.g., N f The duration of a symbol period may depend on the subcarrier spacing or the frequency band of operation.

[0075]

[0088] A subframe, slot, minislot, or symbol may be the smallest scheduling unit (e.g., in the time domain) of the wireless communications system 100 and may be referred to as a transmission time interval (TTI). In some examples, the TTI duration (e.g., the number of symbol periods in a TTI) may be variable. Additionally or alternatively, the smallest scheduling unit of the wireless communications system 100 may be dynamically selected (e.g., among a burst of shortened TTIs (sTTIs)).

[0076]

[0089] The physical channels may be multiplexed on the carriers according to various techniques. The physical control channels and the physical data channels may be multiplexed on the downlink carriers using, for example, one or more of a time division multiplexing (TDM) technique, a frequency division multiplexing (FDM) technique, or a hybrid TDM-FDM technique. A control region (e.g., a control resource set (CORESET)) for the physical control channels may be defined by a number of symbol periods and may extend across the system bandwidth of the carrier or a subset of the system bandwidth. One or more control regions (e.g., CORESETs) may be configured for a set of UEs 115. For example, one or more of the UEs 115 may monitor or search the control region for control information according to one or more search space sets, and each search space set may include one or more control channel candidates at one or more aggregation levels configured in a cascaded manner. The aggregation level for a control channel candidate may refer to a number of control channel resources (e.g., control channel elements (CCEs)) associated with coded information for a control information format having a given payload size. The search space set may include a common search space set configured for sending control information to multiple UEs 115 and a UE-specific search space set for sending control information to a specific UE 115.

[0077]

[0090] Each base station 105 may provide communication coverage via one or more cells, e.g., macro cells, small cells, hot spots, or other types of cells, or any combination thereof. The term "cell" may refer to a logical communication entity used for communication (e.g., on a carrier) with the base station 105 and may be associated with an identifier (e.g., a physical cell identifier (PCID), a virtual cell identifier (VCID), or other) to distinguish neighboring cells. In some examples, a cell may also refer to a geographical coverage area 110 or a portion (e.g., a sector) of a geographical coverage area 110 in which the logical communication entity operates. Such a cell may range from a smaller area (e.g., a structure, a subset of a structure) to a larger area, depending on various factors such as the capabilities of the base station 105. For example, a cell may be or include, among others, a building, a subset of a building, or an outside space between or overlapping with the geographical coverage area 110.

[0078]

[0091] A macro cell generally covers a relatively large geographic area (e.g., a radius of several kilometers) and may allow unrestricted access by UEs 115 that subscribe to the service of the network provider that supports the macro cell. A small cell may be associated with a lower power base station 105 compared to a macro cell, and the small cell may operate in the same or a different (e.g., licensed, unlicensed) frequency band as the macro cell. A small cell may provide unrestricted access to UEs 115 that subscribe to the service of the network provider, or may provide restricted access to UEs 115 that have an association with the small cell (e.g., UEs 115 in a closed subscriber group (CSG), UEs 115 associated with a user in a home or office). A base station 105 may support one or more cells and may support communication on one or more cells using one or more component carriers.

[0079]

[0092] In some examples, a carrier may support multiple cells, and different cells may be configured according to different protocol types (e.g., MTC, narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB)) that may provide access to different types of devices.

[0080]

[0093] In some examples, the base stations 105 may be mobile and thus provide communication coverage to moving geographic coverage areas 110. In some examples, different geographic coverage areas 110 associated with different technologies may overlap, but the different geographic coverage areas 110 may be supported by the same base station 105. In other examples, overlapping geographic coverage areas 110 associated with different technologies may be supported by different base stations 105. The wireless communication system 100 may include a heterogeneous network, for example, where different types of base stations 105 provide coverage to various geographic coverage areas 110 using the same or different radio access technologies.

[0081]

[0094] The wireless communications system 100 may support synchronous or asynchronous operation. For synchronous operation, the base stations 105 may have similar frame timing and transmissions from different base stations 105 may be approximately aligned in time. For asynchronous operation, the base stations 105 may have different frame timing and transmissions from different base stations 105 may not be aligned in time, in some examples. The techniques described herein may be used for either synchronous or asynchronous operation.

[0082]

[0095] Some UEs 115, such as MTC or IoT devices, may be low-cost or low-complexity devices and may provide automated communication between machines (e.g., via Machine-to-Machine (M2M) communication). M2M communication or MTC may refer to data communication technologies that allow devices to communicate with each other or with the base station 105 without human intervention. In some examples, M2M communication or MTC may include communication from devices that incorporate sensors or meters to measure or capture information and relay that information to a central server or application program that utilizes such information or presents the information to a human who interacts with the application program. Some UEs 115 may be designed to collect information or enable automated behavior of machines or other devices. Examples of applications for MTC devices include smart metering, inventory monitoring, water level monitoring, equipment monitoring, healthcare monitoring, wildlife monitoring, weather and geological event monitoring, fleet management and tracking, remote security sensing, physical access control, and transaction-based business billing.

[0083]

[0096] Some UEs 115 may be configured to employ operating modes that reduce power consumption, such as half-duplex communication (e.g., a mode that supports one-way communication via transmission or reception, but not simultaneous transmission and reception). In some examples, half-duplex communication may be implemented at a reduced peak rate. Other power saving techniques for the UE 115 include entering a power saving deep sleep mode when not engaged in active communication, operating over a limited bandwidth (e.g., pursuant to narrowband communication), or a combination of these techniques. For example, some UEs 115 may be configured for operation using a narrowband protocol type associated with a defined portion or range (e.g., a set of subcarriers or resource blocks (RBs)) within a carrier, within a guard band of the carrier, or outside of a carrier.

[0084]

[0097] The wireless communication system 100 may be configured to support ultra-reliable or low-latency communications, or various combinations thereof. For example, the wireless communication system 100 may be configured to support ultra-reliable low-latency communications (URLLC). The UEs 115 may be designed to support ultra-reliable, low-latency, or critical functions. Ultra-reliable communications may include private or group communications and may be supported by one or more services, such as push-to-talk, video, data, etc. Support for ultra-reliable, low-latency functionality may include service prioritization, and such services may be used for public safety or general commercial applications. The terms ultra-reliable, low latency, and ultra-reliable low latency may be used interchangeably herein.

[0085]

[0098] In some examples, the UE 115 may also be able to communicate directly with other UEs 115 via a device-to-device (D2D) communication link 135 (e.g., using a peer-to-peer (P2P) protocol or a D2D protocol). One or more UEs 115 utilizing D2D communication may be within the geographic coverage area 110 of the base station 105. Other UEs 115 in such a group may be outside the geographic coverage area 110 of the base station 105 or may not otherwise be able to receive transmissions from the base station 105. In some examples, a group of UEs 115 communicating via D2D communication may utilize a one-to-many (1:M) system in which each UE 115 transmits to every other UE 115 in the group. In some examples, the base station 105 facilitates scheduling of resources for D2D communication. In other cases, D2D communication is performed between UEs 115 without the involvement of the base station 105.

[0086]

[0099] In some systems, the D2D communication link 135 may be an example of a communication channel, such as a sidelink communication channel, between vehicles (e.g., UE 115). In some examples, the vehicles may communicate using vehicle-to-everything (V2X) communication, vehicle-to-vehicle (V2V) communication, or some combination thereof. The vehicles may signal information related to traffic conditions, signal scheduling, weather, safety, emergency situations, or any other information related to the V2X system. In some examples, the vehicles in the V2X system may communicate with roadside infrastructure, such as roadside units, or with a network via one or more network nodes (e.g., base stations 105) using vehicle-to-network (V2N) communication, or both.

[0087]

[0100] The core network 130 may provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. The core network 130 may be an evolved packet core (EPC) or a 5G core (5GC), which may include at least one control plane entity (e.g., a mobility management entity (MME), an access and mobility management function (AMF)) that manages access and mobility, and at least one user plane entity (e.g., a serving gateway (S-GW), a Packet Data Network (PDN) gateway (P-GW), or a user plane function (UPF)) that routes packets or interconnects to external networks. The control plane entity may manage non-access stratum (NAS) functions such as mobility, authentication, and bearer management for UEs 115 served by base stations 105 associated with the core network 130. User IP packets may be forwarded through a user plane entity, which may provide IP address allocation as well as other functions. The user plane entity may be connected to IP services 150 for one or more network operators. The IP services 150 may include access to the Internet, an intranet(s), an IP Multimedia Subsystem (IMS), or packet-switched streaming services.

[0088]

[0101] Some of the network devices, such as the base stations 105, may include subcomponents, such as an access network entity 140, which may be an example of an access node controller (ANC). Each access network entity 140 may communicate with the UE 115 through one or more other access network transmitting entities 145, which may be referred to as radio heads, smart radio heads, or transmission / reception points (TRPs). Each access network transmitting entity 145 may include one or more antenna panels. In some configurations, various functions of each access network entity 140 or base station 105 may be distributed across various network devices (e.g., radio heads and ANCs) or integrated into a single network device (e.g., the base station 105).

[0089]

[0102] Thus, as described herein, a base station 105 or network entity may include one or more components located in a single physical location, or one or more components located in various physical locations. In embodiments in which a base station 105 includes components located in various physical locations, the various components may each perform various functions such that the various components collectively achieve similar functionality as a base station 105 located in a single physical location. Thus, a base station 105 as described herein may equivalently refer to a standalone base station 105 (also known as a monolithic base station) or a base station 105 including components located in various physical or virtualized locations (also known as a disaggregated base station). In some implementations, such a base station 105 including components located in various physical locations may be referred to as or associated with a disaggregated radio access network (RAN) architecture, such as an open RAN (O-RAN) or virtualized RAN (VRAN) architecture. In some implementations, such components of the base station 105 may include or refer to one or more of a central unit (or centralized unit, CU), a distributed unit (DU), or a radio unit (RU). A network entity may directly obtain or output information or signals with the UE 115 (e.g., a RU may directly transmit and receive signals with the UE 115), or a network entity may indirectly obtain or output information for signals with the UE 115 or through an intermediate device (e.g., a DU may receive information or signals from the UE 115 via a RU).

[0090]

[0103] In some examples, the base station 105 or network entity may be implemented in a disaggregated architecture (e.g., disaggregated base station architecture, disaggregated RAN architecture) that may be configured to utilize a protocol stack that is physically or logically distributed between two or more network entities, such as an IAB network, an O-RAN (e.g., a network configuration provided by the O-RAN Alliance), or a VRAN (e.g., Cloud RAN (C-RAN)). For example, the network entity may include one or more of a CU, a DU, a RU, a RAN Intelligent Controller (RIC) (e.g., Near Real-Time RIC (Near RT RIC), Non-Real-Time RIC (Non-RT RIC)), a Service Management and Orchestration (SMO) system, or any combination thereof. The RU may also be referred to as a radio head, a smart radio head, a remote radio head (RRH), a remote radio unit (RRU), or a transmit / receive point (TRP). One or more components of the network entity in a disaggregated RAN architecture may be collocated, or one or more components of the network entity may be located in distributed locations (e.g., separate physical locations). In some examples, one or more network entities of the disaggregated RAN architecture may be implemented as a virtual unit (e.g., a Virtual CU (VCU), a Virtual DU (VDU), a Virtual RU (VRU)).

[0091]

[0104] The division of functions among the CU, DU, and RU is flexible and may support different functions depending on which functions (e.g., network layer functions, protocol layer functions, baseband functions, radio frequency (RF) functions, and any combination thereof) are executed in the CU, DU, or RU. For example, a functional division of a protocol stack may be adopted between the CU and DU such that the CU may support one or more layers of the protocol stack and the DU may support one or more different layers of the protocol stack. In some examples, the CU may host higher protocol layer (e.g., Layer 3 (L3), Layer 2 (L2)) functions and signaling (e.g., Radio Resource Control (RRC), Service Data Adaptation Protocol (SDAP), Packet Data Convergence Protocol (PDCP)). A CU may be connected to one or more DUs or RUs, which may host lower protocol layers such as Layer 1 (L1) (e.g., physical (PHY) layer) or L2 (e.g., radio link control (RLC) layer, medium access control layer) functions and signaling, each of which may be at least partially controlled by a CU. Additionally or alternatively, a functional division of a protocol stack may be employed between the DU and the RU, such that the DU may support one or more layers of the protocol stack, and the RU may support one or more different layers of the protocol stack. The DU may support one or more different cells (e.g., via one or more RUs). In some cases, the functional division between the CU and the DU or between the DU and the RU may be within a protocol layer (e.g., some functions for a protocol layer may be performed by one of the CU, DU, or RU, while other functions of the protocol layer are performed by a different one of the CU, DU, or RU). The CU may be further functionally divided into a CU control plane (CU-CP) function and a CU user plane (CU-UP) function. The CU may be connected to one or more DUs via midhaul communication links (e.g., F1, F1-c, F1-u), and the DUs may be connected to one or more RUs via fronthaul communication links (e.g., an open fronthaul (FH) interface).In some examples, a midhaul or fronthaul communication link may be implemented according to an interface (e.g., a channel) between layers of a protocol stack supported by the respective network entities communicating over such communication link.

[0092]

[0105] The wireless communication system 100 may operate using one or more frequency bands, typically in the range of 300 megahertz (MHz) to 300 gigahertz (GHz). The 300 MHz to 3 GHz region is commonly known as the ultra-high frequency (UHF) region or decimeter band, because the wavelengths range from approximately 1 decimeter to 1 meter in length. Although UHF waves may be blocked or redirected by buildings and environmental features, the waves may penetrate structures well enough for a macrocell to serve UEs 115 located indoors. Transmission of UHF waves may be associated with smaller antennas and shorter distances (e.g., less than 100 kilometers) compared to transmissions using lower frequencies and longer waves in the shortwave (high frequency (HF) or very high frequency (VHF) portions of the spectrum below 300 MHz.

[0093]

[0106] The wireless communication system 100 may also operate in the super high frequency (SHF) region using a frequency band from 3 GHz to 30 GHz, also known as the centimeter band, or in the extremely high frequency (EHF) region of the spectrum (e.g., from 30 GHz to 300 GHz), also known as the millimeter band. In some examples, the wireless communication system 100 may support millimeter wave (mmW) communications between the UE 115 and the base station 105, and the EHF antennas of the respective devices may be smaller and more closely spaced than UHF antennas. In some examples, this may facilitate the use of antenna arrays within the devices. However, propagation of EHF transmissions may experience more atmospheric attenuation and shorter distances than SHF or UHF transmissions. The techniques disclosed herein may be employed over transmissions using one or more different frequency regions, and the designated use of the bands over these frequency regions may vary by country or regulatory body.

[0094]

[0107] The electromagnetic spectrum is often subdivided into various classes, bands, channels, etc. based on frequency / wavelength. In 5G NR, two initial operating bands have been identified with frequency range designations FR1 (410 MHz-7.125 GHz) and FR2 (24.25 GHz-52.6 GHz). It should be understood that FR1 is often referred to (interchangeably) as the "sub-6 GHz" band in various documents and papers, although a portion of FR1 is above 6 GHz. A similar nomenclature issue may arise with respect to FR2, which is often referred to (interchangeably) as the "millimeter wave" band in documents and papers, even though it is different from the extremely high frequency (EHF) band (30 GHz-300 GHz) identified as the "millimeter wave" band by the International Telecommunications Union (ITU).

[0095]

[0108] Frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified operating bands for these mid-band frequencies as a frequency range designation FR3 (7.125 GHz to 24.25 GHz). Frequency bands that fall within FR3 may inherit FR1 and / or FR2 characteristics, and thus, in effect, extend the features of FR1 and / or FR2 to the mid-band frequencies. Additionally, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6 GHz to 71 GHz), FR4 (52.6 GHz to 114.25 GHz), and FR5 (114.25 GHz to 300 GHz). Each of these higher frequency bands falls within the EHF band.

[0096]

[0109] With the above aspects in mind, it should be understood that unless specifically stated otherwise, terms such as "sub-6 GHz" as used herein may broadly refer to frequencies that may be below 6 GHz, may be within FR1, or may include mid-band frequencies. Additionally, unless specifically stated otherwise, it should be understood that terms such as "mmWave" as used herein may broadly refer to frequencies that may include mid-band frequencies, may be within the ranges of FR2, FR4, FR4-a or FR4-1, and / or FR5, or may be within the EHF band.

[0097]

[0110] The wireless communication system 100 may utilize both licensed and unlicensed radio frequency spectrum bands. For example, the wireless communication system 100 may utilize License Assisted Access (LAA), LTE-Unlicensed (LTE-U) radio access technology, or NR technology in an unlicensed band, such as the 5 GHz industrial, scientific, and medical (ISM) band. When operating in an unlicensed radio frequency spectrum band, devices such as the base station 105 and the UE 115 may utilize carrier sensing for collision detection and avoidance. In some examples, operation in an unlicensed band may be based on a carrier aggregation configuration in conjunction with component carriers operating in a licensed band (e.g., LAA). Operation in an unlicensed spectrum may include downlink transmissions, uplink transmissions, P2P transmissions, or D2D transmissions, among other examples.

[0098]

[0111] The base station 105 or UE 115 may be equipped with multiple antennas that may be used to utilize techniques such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communications, or beamforming. The antennas of the base station 105 or UE 115 may be located in one or more antenna arrays or antenna panels that may support MIMO operations or transmit or receive beamforming. For example, one or more base station antennas or antenna arrays may be collocated in an antenna assembly such as an antenna tower. In some examples, the antennas or antenna arrays associated with the base station 105 may be located in various geographic locations. The base station 105 may have an antenna array with several rows and columns of antenna ports that the base station 105 may use to support beamforming of communications with the UE 115. Similarly, the UE 115 may have one or more antenna arrays that may support various MIMO or beamforming operations. Additionally or alternatively, the antenna panels may support radio frequency beamforming for signals transmitted through the antenna ports.

[0099]

[0112] A base station 105 or a UE 115 may use MIMO communications to exploit multipath signal propagation and increase spectral efficiency by transmitting or receiving multiple signals via different spatial layers. Such techniques may be referred to as spatial multiplexing. Multiple signals may be transmitted by a transmitting device, for example, via different antennas or different combinations of antennas. Similarly, multiple signals may be received by a receiving device via different antennas or different combinations of antennas. Each of the multiple signals may be referred to as a separate spatial stream and may carry bits related to the same data stream (e.g., the same codeword) or different data streams (e.g., different codewords). Different spatial layers may be associated with different antenna ports used for channel measurement and reporting. MIMO techniques include single-user MIMO (SU-MIMO), in which multiple spatial layers are transmitted to the same receiving device, and multiple-user MIMO (MU-MIMO), in which multiple spatial layers are transmitted to multiple devices.

[0100]

[0113] Beamforming, which may also be referred to as spatial filtering, directional transmission, or directional reception, is a signal processing technique that may be used at a transmitting or receiving device (e.g., base station 105, UE 115) to shape or steer an antenna beam (e.g., transmit beam, receive beam) along a spatial path between the transmitting device and the receiving device. Beamforming may be achieved by combining signals communicated through antenna elements of an antenna array such that some signals propagating at a particular orientation relative to the antenna array are subject to constructive interference, while other signals are subject to destructive interference. Adjustment of signals communicated through antenna elements may include a transmitting or receiving device applying an amplitude offset, a phase offset, or both to signals carried through an antenna element associated with the device. The adjustment associated with each of the antenna elements may be defined by a beamforming weight set associated with a particular orientation (e.g., relative to the antenna array of the transmitting or receiving device, or to some other orientation).

[0101]

[0114] The base station 105 or the UE 115 may use beam sweeping techniques as part of a beamforming operation. For example, the base station 105 may use multiple antennas or antenna arrays (e.g., antenna panels) to perform a beamforming operation for directional communication with the UE 115. Some signals (e.g., synchronization signals, reference signals, beam selection signals, or other control signals) may be transmitted by the base station 105 multiple times in different directions. For example, the base station 105 may transmit signals according to different beamforming weight sets associated with different directions of transmission. The transmissions in different beam directions may be used (e.g., by a transmitting device such as the base station 105 or by a receiving device such as the UE 115) to identify beam directions for later transmission or reception by the base station 105.

[0102]

[0115] Some signals, such as data signals associated with a particular receiving device, may be transmitted by the base station 105 in a single beam direction (e.g., a direction associated with a receiving device, such as the UE 115). In some examples, the beam direction associated with a transmission along the single beam direction may be determined based on signals transmitted in one or more beam directions. For example, the UE 115 may receive one or more of the signals transmitted by the base station 105 in different directions and may report to the base station 105 an indication of the signal that the UE 115 received with the highest signal quality, or otherwise acceptable signal quality.

[0103]

[0116] In some examples, transmission by a device (e.g., by the base station 105 or the UE 115) may be performed using multiple beam directions, and the device may use a combination of digital precoding or radio frequency beamforming to generate a combined beam for transmission (e.g., from the base station 105 to the UE 115). The UE 115 may report feedback indicating precoding weights for one or more beam directions, and the feedback may correspond to a configured number of beams across the system bandwidth or one or more subbands. The base station 105 may transmit a reference signal (e.g., cell-specific reference signal (CRS), CSI-RS) that may be precoded or amplify coded. The UE 115 may provide feedback for beam selection, which may be a precoding matrix indicator (PMI) or codebook-based feedback (e.g., multi-panel type codebook, linear combination type codebook, port selection type codebook). Although these techniques are described with reference to signals transmitted by the base station 105 in one or more directions, the UE 115 may employ similar techniques to transmit a signal multiple times in different directions (e.g., to identify a beam direction for subsequent transmission or reception by the UE 115) or to transmit a signal in a single direction (e.g., to transmit data to a receiving device).

[0104]

[0117] A receiving device (e.g., UE 115) may attempt multiple receive configurations (e.g., directional listening) when receiving various signals, such as synchronization signals, reference signals, beam selection signals, or other control signals, from the base station 105. For example, the receiving device may attempt multiple receive directions by receiving via different antenna subarrays, by processing the received signals according to different antenna subarrays, by receiving according to different receive beamforming weight sets (e.g., different directional listening weight sets) applied to signals received at multiple antenna elements of the antenna array, or by processing the received signals according to different receive beamforming weight sets applied to signals received at multiple antenna elements of the antenna array, any of which may be referred to as "listening" with different receive configurations or receive directions. In some examples, the receiving device may use a single receive configuration to receive along a single beam direction (e.g., when receiving a data signal). The single receive configuration may be aligned to a beam direction determined based on listening with different receive configuration directions (e.g., a beam direction determined to have the highest signal strength, highest signal-to-noise ratio (SNR), or otherwise acceptable signal quality based on listening with multiple beam directions).

[0105]

[0118] The wireless communication system 100 may be a packet-based network that operates according to a layered protocol stack. In the user plane, communication at the bearer or Packet Data Convergence Protocol (PDCP) layer may be IP-based. The Radio Link Control (RLC) layer may perform packet segmentation and reassembly to communicate on logical channels. The Medium Access Control (MAC) layer may perform priority handling and multiplexing of logical channels into transport channels. The MAC layer may also use error detection techniques, error correction techniques, or both to support retransmissions at the MAC layer to improve link efficiency. In the control plane, the RRC protocol layer may establish, configure, and maintain the RRC connection between the UE 115 and the base station 105 or core network 130, which supports radio bearers for user plane data. In the physical layer, the transport channels may be mapped to physical channels.

[0106]

[0119] The UE 115 and the base station 105 may support retransmission of data to increase the likelihood of successful reception of the data. Hybrid automatic repeat request (HARQ) feedback is one technique for increasing the likelihood that data is correctly received on the communication link 125. HARQ may include a combination of error detection (e.g., using a cyclic redundancy check (CRC)), forward error correction (FEC), and retransmission (e.g., automatic repeat request (ARQ)). HARQ may improve throughput at the MAC layer in poor radio conditions (e.g., low signal-to-noise conditions). In some examples, a device may support same-slot HARQ feedback, in which the device may provide HARQ feedback in a particular slot for data received in a previous symbol in that slot. In other cases, the device may provide HARQ feedback in a subsequent slot or according to some other time interval.

[0107]

[0120] In addition to or as an alternative to being performed between the UE 115 and the base station 105, the techniques described herein may be implemented via additional or alternative wireless devices, including the IAB node 104, the DU 165, the CU 160, the RU 170, etc. For example, in some implementations, aspects described herein may be implemented in the context of a disaggregated RAN architecture (e.g., an open RAN architecture). In a disaggregated architecture, the RAN may be divided into three areas of functionality corresponding to the CU 160, the DU 165, and the RU 170. The division of functionality among the CU 160, the DU 165, and the RU 175 is flexible and thus results in numerous permutations of different functionality depending on which functionality (e.g., MAC functionality, baseband functionality, radio frequency functionality, and any combination thereof) is performed in the CU 160, the DU 165, and the RU 175. For example, functional division of the protocol stack may be adopted between DU 165 and RU 170 such that DU 165 may support one or more layers of the protocol stack and RU 170 may support one or more different layers of the protocol stack.

[0108]

[0121] In some wireless communication systems (e.g., wireless communication system 100), infrastructure and spectrum resources for NR access may supplement wired backhaul connections to additionally support wireless backhaul link capabilities to provide an IAB network architecture. One or more base stations 105 may include a CU 160, a DU 165, and an RU 170 and may be referred to as a donor base station 105 or an IAB donor. One or more DUs 165 (e.g., and / or RUs 170) associated with a donor base station 105 may be controlled in part by a CU 160 associated with the donor base station 105. One or more donor base stations 105 (e.g., IAB donors) may communicate with one or more additional base stations 105 (e.g., IAB nodes 104) via supported access and backhaul links. An IAB node 104 may support mobile terminal (MT) functions controlled and / or scheduled by the DU 165 of the associated IAB donor. In addition, the IAB node 104 may include a DU 165 that supports communication links with additional entities (e.g., the IAB node 104, the UE 115, etc.) in a relay chain or configuration of an access network (e.g., downstream). In such cases, one or more components of the disaggregated RAN architecture (e.g., one or more IAB nodes 104 or components of the IAB node 104) may be configured to operate in accordance with the techniques described herein.

[0109]

[0122] In some embodiments, the wireless communications system 100 may include a core network 130 (e.g., a next generation core network (NGC)), one or more IAB donors, IAB nodes 104, and UEs 115, where the IAB nodes 104 may be controlled in part by each other and / or the IAB donors. The IAB donors and IAB nodes 104 may be examples of aspects of a base station 105. The IAB donors and the one or more IAB nodes 104 may be configured as (e.g., communicate according to) some relay chain.

[0110]

[0123] For example, an access network (AN) or RAN may refer to communication between an access node (e.g., IAB donor), an IAB node 104, and one or more UEs 115. An IAB donor may facilitate a connection between the core network 130 and an AN (e.g., via a wired or wireless connection to the core network 130). That is, an IAB donor may refer to a RAN node that has a wired or wireless connection to the core network 130. An IAB donor may include a CU 160 and at least one DU 165 (e.g., and RU 170), and the CU 160 may communicate with the core network 130 via an NG interface (e.g., some backhaul link). The CU 160 may host layer 3 (L3) (e.g., RRC, service data adaptation protocol (SDAP), PDCP, etc.) functions and signaling. At least one DU 165 and / or RU 170 may host lower layers such as Layer 1 (L1) and Layer 2 (L2) (e.g., RLC, MAC, physical (PHY), etc.) functions and signaling, each of which may be at least partially controlled by the CU 160. The DU 165 may support one or more different cells. The IAB donor and the IAB node 104 may communicate over an F1 interface according to some protocol (e.g., F1 AP protocol) that defines signaling messages. Additionally, the CU 160 may communicate with the core network over an NG interface (which may be an example of a portion of a backhaul link) and with other CUs 160 (e.g., CUs 160 associated with alternative IAB donors) over an Xn-C interface (which may be an example of a portion of a backhaul link).

[0111]

[0124] An IAB node 104 may refer to a RAN node that provides IAB functionality (e.g., access for UEs 115, wireless self-backhaul capabilities, etc.). An IAB node 104 may include a DU 165 and an MT. The DU 165 may act as a distributed scheduling node with respect to a child node associated with the IAB node 104, and the MT may act as a scheduled node with respect to a parent node associated with the IAB node 104. That is, an IAB donor may be referred to as a parent node that communicates with one or more child nodes (e.g., an IAB donor may relay a transmission for a UE through one or more other IAB nodes 104). Additionally, an IAB node 104 may be referred to as a parent node or a child node with respect to other IAB nodes 104 depending on the relay chain or configuration of the AN. Thus, the MT entity of the IAB node 104 (e.g., MT) may provide a Uu interface for a child node to receive signaling from a parent IAB node 104, and the DU interface (e.g., DU 165) may provide a Uu interface for a parent node to signal to a child IAB node 104 or UE 115.

[0112]

[0125] For example, the IAB node 104 may be referred to as a parent node associated with the IAB node and a child node associated with the IAB donor. The IAB donor may include a CU 160 having a wired (e.g., optical fiber) or wireless connection to the core network and may act as a parent node to the IAB node 104. For example, the DU 165 of the IAB donor may relay a transmission to the UE 115 via the IAB node 104 and may directly signal a transmission to the UE 115. The CU 160 of the IAB donor may signal a communication link establishment to the IAB node 104 via the F1 interface, and the IAB node 104 may schedule a transmission (e.g., a transmission to the UE 115 relayed from the IAB donor) via the DU 165. That is, data may be relayed to and from the IAB node 104 via signaling over the NR Uu interface to the MT of the IAB node 104. Communications with the IAB node 104 may be scheduled by the DU 165 of the IAB donor, and communications with the IAB node 104 may be scheduled by the DU 165 of the IAB node 104.

[0113]

[0126] For the techniques described herein applied in the context of a disaggregated RAN architecture, one or more components of the disaggregated RAN architecture (e.g., one or more IAB nodes 104 or components of an IAB node 104) may be configured to support techniques for large round trip times in random access channel procedures described herein. For example, some operations described as being performed by a UE 115 or a base station 105 may additionally or alternatively be performed by a component of the disaggregated RAN architecture (e.g., an IAB node, a DU, a CU, etc.).

[0114]

[0127] The UE 115 may measure interference at the UE 115 using interference measurement resources such as CSI-RS, CSI-IM, or through IMR. The interference at the UE 115 may be caused, for example, by communications at neighboring base stations 105 or sidelink communications between other UEs 115. In NR, the slot structure may be more flexible compared to the slot structure in LTE. For example, NR may include mini-slots and URLLC slots. In some examples, short bursts of transmissions within a normal enhanced mobile broadband (eMBB) slot may start at any symbol position. In NR, unscheduled uplink transmissions from the UE without permission from the base station may occur. NR may also include highly adaptive reference signal patterns (e.g., demodulation reference signal (DMRS) and CSI-RS patterns may depend on the number of available antenna ports, delay tolerance, or Doppler spread). Thus, interference may vary significantly between one UE 115 and another UE 115.

[0115]

[0128] The interference at the UE 115 may vary in the time, frequency, and spatial domains, and the interference at the UE 115 may also have correlation in the time, frequency, and spatial domains. For example, correlation characteristics of interference observed at a particular UE 115 may vary depending on scheduling decisions at neighboring base stations 105. The UE 115 may predict interference on future communication resources based on interference correlations learned from previous communication resources. The interference measured at the UE 115 may be reported to the serving base station 105 via a CSF report. For example, the CSF report may include a rank index (RI), a channel quality index (CQI), and a PMI. The RI, CQI, and PMI may take into account the interference level and channel estimation at the UE 115. The CSF report (e.g., including RI, CQI, and PMI) may not report information about the time correlation characteristics, frequency correlation characteristics, or spatial correlation characteristics of the interference. Thus, the serving base station 105 may not exploit time, frequency, or spatial correlation characteristics of the interference at the UE 115 when scheduling communications with the UE 115 based on the CSF report. Because the variation of the interference at the UE 115 may be large (e.g., larger than the channel variation reported in the CSF report), explicitly reporting the interference distribution to the serving base station may be associated with a large resource overhead and may be difficult to parameterize across different UEs.

[0116]

[0129] In some examples, the UE 115-a may include a communications manager 102 configured to support one or more aspects of the techniques for compression and reconstruction of interference distributions described herein. For example, the UE 115 may transmit compressed interference information to the base station 105 via the communications manager 102 to reduce resource overhead associated with explicit interference distribution reporting while allowing the base station 105 to take interference correlation into account when scheduling communications with the UE 115. In some examples, the base station 105 may include a communications manager 101 configured to support one or more aspects of the techniques for compression and reconstruction of interference distributions described herein. For example, the base station 105 may receive compressed interference information via the communications manager 101.

[0117]

[0130] The UE 115 may measure interference at the UE 115 across the set of interference measurement resources. The UE 115 may then determine an interference distribution across the set of resources. In some examples, the interference distribution may be one of a probability density function or a probability mass function. The UE 115 may encode the interference distribution using a compression scheme that may reduce the payload or size of the interference distribution. The compression scheme may include compressing the distribution of the measured interference across the given set of interference measurement resources. In some examples, encoding the interference information according to the compression scheme may include generating a mean vector and a covariance matrix of latent random variables that represent the distribution of the measured interference across the given set of interference measurement resources. The mean vector and the covariance matrix of the latent random variables may indicate a distribution of a probabilistic latent random vector associated with the measured interference. The UE 115 may transmit the encoded interference information via the communications manager 102, and the base station 105 may receive and decode the encoded interference information via the communications manager 101. The base station 105 may schedule communication with the UE 115 based on the interference information via the communications manager 101.

[0118]

[0131] In some examples, the UE 115 or base station 105 may predict future interference distribution based on past interference measurements at the UE 115 (e.g., predicted across a time / frequency grid for future symbols / slots). For example, the base station 105 may avoid allocating certain resources to the UE 115 if interference in those resources is predicted to be high. For available resources (e.g., resources with low predicted interference), the UE 115 or base station 105 may predict a time / frequency correlation of interference that may be used for demodulation of communications involving the UE 115.

[0119]

[0132] In some examples, the UE 115 may receive control signaling via the communications manager 102 to configure aspects of the interference measurement resources and encoding. For example, the base station 105 may send control signaling via the communications manager 101 to configure the interference measurement resources for the UE 115, the compression scheme, the parameters of the compression scheme, the encoding configuration for the encoder, and / or the input format to the encoder. In some examples, the base station 105 may adjust the interference measurement resources for the UE 115, the compression scheme, the parameters of the compression scheme, the encoder, and / or the input format to the encoder based on the location of the UE 115 (e.g., a UE 115 at the edge of the cell 110 may experience higher interference than a UE 115 at the cell center). In some examples, the base station 105 may adjust the interference measurement resources for the UE 115, the compression scheme, the parameters of the compression scheme, the encoder, and / or the input format to the encoder based on knowledge of the interference pattern. For example, the interference pattern may depend on external factors such as the number of active UEs 115, transmission configuration indications (TCIs), beams, and / or loading in neighboring cells 110.

[0120]

[0133] The UE 115 may include an artificial NN that may update model parameters associated with a compression scheme based on past interference measurements at the UE. Artificial NNs that may support such machine learning techniques for channel compression include fully connected NNs, batch normalized NNs, dropout NNs, convolutional NNs, residual NNs, ReLU NNs, and other types of NNs. The UE 115 may report the learned or updated model parameters to the base station. The base station 105 may receive the learned or updated model parameters from multiple UEs 115. The base station 105 may update parameters associated with encoding or decoding based on the learned parameters received from multiple UEs 115.

[0121]

[0134] 2 illustrates an example of a wireless communication system 200 supporting compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. The wireless communication system 200 illustrates an example of communication between a network entity 205-a associated with a first coverage area 110-a, a network entity 205-b associated with a second coverage area 110-b, and a UE 115-a, a UE 115-b, and a UE 115-c, which may be examples of corresponding devices described herein, including with reference to FIG. 1. For example, the network entity 205-a and / or the network entity 205-b may include all or some components of a base station 105 described herein.

[0122]

[0135] In an example of a wireless communication system 200, the UE 115-a, one or more components of the network entity 205-a, or both may perform an estimation of signal propagation conditions between the network entity 205-a and the UE 115-a, which may be referred to as channel estimation. The signal propagation conditions may refer to a path loss for the signal. For example, one or more components of the network entity 205-a may transmit downlink signaling 210-a, which may include a reference signal 215 (e.g., CSI-RS, or CRS, or another reference signal, or a combination of reference signals). The UE 115-a may monitor such reference signals, and the UE 115-a may perform calculations based on measured or predicted characteristics (e.g., signal-to-noise ratio, signal-to-interference-and-noise ratio, reference signal received power) of the reference signal 215 of the downlink signaling 210-a to support various techniques for channel estimation. Based on monitoring or receiving the reference signal 215, the UE 115-a may transmit uplink signaling 220-a (e.g., a response uplink transmission) that may be received by the network entity 205-a. The uplink signaling 220-a may include a CSF report 225 that may be part of an uplink control information (UCI) transmission (e.g., on a physical uplink control channel (PUCCH)) by the UE 115-a, may include a report of channel conditions based at least in part on channel estimation performed by the UE 115-a, or may include a measurement or an indication of a measurement of the reference signal 215 performed by the UE 115-a (e.g., to support a channel estimation calculation by the network entity 205-a), among other information or combinations of channel information.

[0123]

[0136] As described herein, the UE 115-a may experience interference. For example, the interference may be caused by communication at a neighboring network entity 205-b (e.g., downlink signaling 210-b or uplink signaling 220-b between the neighboring network entity 205-b and the UE 115-b) or sidelink communication between neighboring UEs 115 (e.g., sidelink signaling between the UE 115-b and the UE 115-c via the sidelink communication link 135-a). The UE 115-a may measure the interference at the UE 115-a using the interference measurement manager 245. For example, the interference measurement manager 245 may measure the interference using interference measurement resources 280 (e.g., through CSI-RS, CSI-IM, or IMR) allocated to measure the interference at the UE 115-a. The UE 115-a may determine an interference distribution 255 based on the measured interference.

[0124]

[0137] The UE 115-a may transmit a message 235 including interference information representative of an interference distribution 255 to the network entity 205-a, where the interference distribution is encoded according to a compression scheme. The UE 115-a may include an encoder 240 for encoding the interference information according to a compression scheme. The encoder 240 may refer to software, firmware, or hardware, or any combination thereof, operable to encode the interference information according to a compression scheme. Encoding the interference information according to a compression scheme may reduce resource overhead associated with explicit interference distribution reporting while allowing the network entity 205-a to consider interference correlation when scheduling communications with the UE 115-a. The UE 115-a may measure interference at the UE 115-a across a set of interference measurement resources. The UE 115-a may determine an interference distribution 255 across a set of interference measurement resources 280 using an interference measurement manager 245. The UE 115-a may encode the interference information representative of the interference distribution 255 using a compression scheme, which may reduce the payload or size of the interference information. The compression scheme may include compressing a distribution of the measured interference over a given set of interference measurement resources. In some examples, the distribution of interference may be one of a probability density function or a probability mass function. In some examples, encoding the interference information according to the compression scheme may include generating a mean vector and a covariance matrix of latent random variables that represent the distribution of the measured interference over the given set of interference measurement resources. The UE 115-a may transmit the encoded interference information via a message 235, and the network entity 205-a may receive the message 235 and decode the included encoded interference information. The network entity 205-a may transmit scheduling information 275 for communication with the UE 115-a based on the interference information received in the message 235.

[0125]

[0138] In some examples, the encoder 240 may be configured according to various machine learning techniques (e.g., for neural network-based interference information compression schemes when operating as or otherwise in accordance with an autoencoder), where such techniques may be executed by one or both of the network entity 205-a or the UE 115-a.

[0126]

[0139] The compression scheme may also involve a decoder 230 at the network entity 205-a, which may refer to software, firmware, or hardware, or any combination thereof, operable to decompress the encoded interference information in messages 235 received in uplink signaling 220-a by the network entity 205-a. In some examples, the decoder 230 may be configured according to various machine learning techniques (e.g., for neural network-based interference information compression schemes, when operating as or otherwise according to an autoencoder), where such techniques may be executed by one or both of the network entity 205-a or the UE 115-a.

[0127]

[0140] In some examples, the UE 115-a or the network entity 205-a may predict a future interference distribution based on past interference measurements at the UE 115-a (e.g., predicted across a time / frequency grid for future symbols / slots). For example, the network entity 205-a may avoid allocating certain resources to the UE 115-a if interference in those resources is predicted to be high. For available resources (e.g., resources with low predicted interference), the UE 115-a or the network entity 205-a may predict a time / frequency correlation of interference that may be used for demodulation of communications involving the UE 115-a. For example, in the time domain, the 120th symbol of the interference distribution 255 may be associated with low interference.

[0128]

[0141] In some examples, the UE 115-a may receive control signaling 260 that configures interference measurement resources and coding aspects. The control signaling 260 may be transmitted dynamically through a radio resource control message, a MAC control element (MAC-CE) message, or through a downlink control information (DCI) message. For example, the network entity 205-a may configure interference measurement resources 280 for the UE 115-a, parameters of a compression scheme 281, a coding configuration 282 for the encoder, an input format 283 to the encoder, and / or parameters associated with measuring interference 284. In some examples, the network entity 205-a may adjust the interference measurement resources 280, parameters of a compression scheme 281, a coding configuration, an input format 283 to the encoder, and / or parameters associated with measuring interference 284 for the UE 115-a based on the location of the UE 115-a (e.g., a UE 115-a at the edge of a cell associated with the network entity 205-a may experience higher interference than a UE 115-a at the cell center). In some examples, the network entity 205-a may adjust interference measurement resources 280 for the UE 115-a, parameters of the compression scheme 281, coding configuration 282, input format to the encoder 283, and / or parameters associated with measuring interference 284 based on knowledge of the interference pattern. For example, the interference pattern may depend on external factors such as the number of active UEs 115, transmission configuration indications (TCIs), beams, and / or load associated with a neighboring network entity 205-b.

[0129]

[0142] For example, the network entity 205-a may configure the coding configuration 282 (e.g., for the encoder 240) in the UE 115-a via the control signaling 260. The network entity 205-a may use a corresponding decoder to reconstruct interference information representing the interference distribution received as encoded interference information from the UE 115-a. In some examples, the configured coding configuration 282 (which configures the encoder 240) and the corresponding decoder 230 may be or include an artificial neural network or an autoencoder. The autoencoder may be used to reduce the size of the input to a smaller representation, and then use the compressed version and code to restore the original data.

[0130]

[0143] In some examples, the network entity 205-a may configure parameters 281 of a compression scheme in the UE 115-a via control signaling 260. For example, in the case of a machine learning based encoder and decoder, the parameters 281 of the compression scheme may be a code size, a number of layers, a number of nodes per layer, and / or a loss function used in the UE 115-a to compress the interference information. The code size may represent a trade-off between the overhead size associated with the interference report and the interference distribution reconstruction error.

[0131]

[0144] In some examples, the network entity 205-a may configure parameters 284 associated with measuring interference at the UE 115-a via control signaling 260. For example, the network entity 205-a may configure the time, frequency, and / or space, e.g., beams, resources to be used for interference measurement, estimation, and / or prediction. In some examples, the network entity 205-a may configure the granularity of the interference estimation and / or prediction. For example, the network entity 205-a may configure, for frequency granularity, whether the interference estimation is based on full band or sub-band (and different granularity for different sub-bands). As another example, the network entity 205-a may configure, for time granularity, whether the interference estimation is based on symbol level interference, slot level interference, or multi-slot level interference. As another example, for spatial granularity, the network entity 205-a may configure the interference estimation for a particular beam.

[0132]

[0145] In some examples, the network entity 205-a may configure the input format 283 for the encoder 240 via the control signaling 260. For example, the input format 283 may include an estimated interference distribution, a zero-power or non-zero-power CSI-RS for interference measurement, and / or a measured estimated interference on a previous interference measurement resource.

[0133]

[0146] In some examples, the UE 115-a may transmit a capability message 265 indicating the UE 115-a's capability to encode interference information. For example, the UE 115-a may indicate one or more types of encoders and / or encoding parameters that the UE 115-a may support. In some examples, the capability message 265 may include a recommended encoding configuration 285. For example, the UE 115-a may indicate an index associated with a recommended encoder (e.g., from a set of encoders each associated with a respective index in the table 290). For example, the UE 115-a and the network entity 205-a may each be configured with a table or index that stores the type of encoder and / or the encoding configuration. In response, in some examples, the network entity 205-a may transmit control signaling 260 to configure an aspect of the encoding (e.g., the encoding configuration 282, the compression scheme parameters 281, or the input format 283) based on the indicated capability of the UE 115-a.

[0134]

[0147] In some examples, the output of the encoder 240 may be a condensed estimated or predicted interference distribution for a set of resources in time, frequency, and space. In some examples, the UE 115-a may report the condensed interference distribution as part of the CSF report. For example, the condensed interference distribution may be included as a quantity in the CSI report (e.g., in NR, tier 1 interference reporting may be added to the CSF report).

[0135]

[0148] In some examples, the output of the encoder 240 may be a mean vector and a covariance matrix representing the distribution of the latent random vector associated with the measured interference, as described herein with reference to FIG. 3b.

[0136]

[0149] Machine learning techniques may be used by the wireless communication system 200 to support interference information compression schemes and may include training an encoder (e.g., training an autoencoder, evaluating or configuring parameters to be used in the encoder 240), encoding information such as encoding interference information, training a decoder (e.g., training an autoencoder, evaluating or configuring parameters to be used in the decoder 230), decoding information such as decoding interference information, or any combination thereof. Such machine learning techniques may include one or more artificial neural networks that may be implemented by one or both of the transmitting device (e.g., the UE 115-a) or the receiving device (e.g., the network entity 205-a). Artificial neural networks that may support such machine learning techniques for channel compression include fully connected neural networks, batch normalized neural networks, dropout neural networks, convolutional neural networks, residual neural networks, ReLU neural networks, and other types of neural networks.

[0137]

[0150] Although machine learning techniques may be implemented by the wireless communication system 200 to train the interference information compression scheme, in some examples, there may be a mismatch between the interference information used for training and the interference information used for inference. For example, the machine learning techniques may be trained according to a known interference distribution (e.g., laboratory conditions, known or predicted parameters, known or predicted hardware characteristics, specific modeling approach) that may not match the device (e.g., hardware characteristics or configuration of the network entity 205-a, UE 115-a, or both) or interference distribution statistics (e.g., interference signals from other UE 115 or network entity 205 affecting downlink signaling 210-a or uplink signaling 220-a, information or payload associated with a given interference distribution) for inference, which may be more complex or may present risks or uncertainties for some machine learning techniques. In some examples, one compression scheme may be inappropriate or otherwise less favorable for carrying interference information compared to another compression scheme.

[0138]

[0151] The UE 115-a may include an artificial NN that may update model parameters associated with a compression scheme based on past interference measurements at the UE. Artificial NNs that may support such machine learning techniques for channel compression include fully connected NNs, batch normalized NNs, dropout NNs, convolutional NNs, residual NNs, ReLU NNs, and other types of NNs. The UE 115-a may report the learned or updated model parameters to the network entity 205-a in an update message 270. The network entity 205-a may receive the learned or updated model parameters from multiple UEs 115. The network entity 205-a may update parameters associated with encoding or decoding based on the learned parameters received from multiple UEs 115.

[0139]

[0152] In some examples, the UE 115-a may report learned interference compression model parameters (e.g., neural network weights) that may assist the network entity 205-a in enhancing a global model for compression of the interference distribution across the UEs 115 served by the network entity 205-a. For example, the network entity 205-a may apply a federated learning approach for compression of the interference distribution.

[0140]

[0153] For example, according to the federated learning approach, the UE 115-a may observe an interference measurement resource (e.g., CSI-RS, CSI-IM, or IMR) and estimate interference from the interference measurement resource. The network entity 205-a may define and configure an architecture for the encoder 240 and the decoder 230. For example, the UE 115-a may store a table 290-b of encoder models in a memory, and the network entity 205-a may signal an index of a selected coding configuration 282 to the UE 115 via control signaling 260. In some examples, the network entity 205-a may store a table 290-a of encoder models in a memory, which may correspond to the table 290-b stored in the memory of the UE 115-a. In some examples, the UE 115-a may signal an index of a selected or recommended encoder model (e.g., coding configuration 286) (e.g., via uplink control information).

[0141]

[0154] The UE 115-a may encode interference information representing an interference distribution across the interference measurement resources and transmit the encoded interference information to the network entity 205-a. The UE 115-a may update machine learning-based model parameters (e.g., model coefficients) for compressing or decompressing the interference distribution (based on sufficient interference measurements). However, in some examples, the UE 115-a continues to encode the interference distribution using the configured weights.

[0142]

[0155] The network entity 205-a may send signaling to the UE 115-a requesting the UE 115-a to report the learned model parameters. In response, the UE 115-a may report the learned model parameters to the network entity 205-a. The network entity 205-a may update a global model for interference prediction. In some examples, the network entity 205-a may send control signaling 260 indicating updated parameters for an interference distribution encoder (e.g., updated coefficients for an existing encoder model defined in a table in a memory of the UE 115-a).

[0143]

[0156] To support transmission of interference information report 235, UE 115-a may include a compression scheme manager 250 operable to select between compression schemes (e.g., indicated by control signaling 260) based on various criteria. In some examples, one of the first or second compression schemes may be configured (e.g., in compression scheme manager 250) as a default or target interference distribution compression scheme (e.g., neural network-based interference distribution compression scheme). In some examples, UE 115-a (e.g., compression scheme manager 250) may switch to a different interference distribution compression scheme (e.g., to a codebook-based interference distribution compression scheme, to a regular codebook, to a legacy codebook, to a fallback interference distribution compression scheme) or otherwise select an interference distribution compression scheme if certain conditions are met or not met.

[0144]

[0157] In some examples, the conditions for evaluating or selecting between a first interference distribution compression scheme and a second interference distribution compression scheme may involve calculating or comparing differences or errors associated with different interference distribution compression schemes (e.g., determining to switch to a normal codebook-based interference distribution compression scheme when a neural network-based interference distribution compression scheme has a higher MSE, or a threshold or comparison of the mean squared error (MSE) of one interference distribution compression scheme or another). In some examples, the operating conditions of UE115-a may be considered (e.g., in compression scheme manager 250) when evaluating or selecting an interference distribution compression scheme, such as based on power availability (e.g., battery status), power consumption (e.g., associated with one interference distribution compression scheme or another), processor availability (e.g., available processing cycles), processor load (e.g., associated with one interference distribution compression scheme or another), or any combination thereof, to evaluate or select an interference distribution compression scheme.

[0145]

[0158] In one example for evaluating an interference distribution compression scheme with respect to power consumption (e.g., associated with performing encoding according to a particular interference distribution compression scheme), a codebook-based interference distribution compression scheme may be associated with power P1, and a neural network-based interference distribution compression scheme may be associated with power P2. The evaluation between the codebook-based interference distribution compression scheme and the neural network-based compression scheme by UE115-a may be associated with a parameter α that can be communicated using control signaling 260. P1 * If the condition α < P2 is satisfied, UE115-a may select encoding or decoding according to the codebook-based interference distribution compression scheme (e.g., configure encoder 240 according to the codebook-based interference information compression scheme, encode interference information according to the codebook-based interference distribution compression scheme, and indicate that decoder 230 should be configured according to the codebook-based interference distribution compression scheme). P1 *If the condition of α < P2 is not satisfied, UE115-a may select encoding or decoding according to a neural network-based interference information compression method (for example, configure the encoder 240 according to an autoencoder, encode the interference distribution according to a neural network-based compression method, and indicate that the decoder 230 should be configured according to a neural network-based interference distribution compression method, and indicate that the decoder 230 should be configured according to an autoencoder).

[0146]

[0159] Additionally or alternatively, in an example for evaluating an interference distribution compression method against a processing load (for example, associated with the processing load for performing encoding according to a specific interference distribution compression method), a codebook-based interference distribution compression method may be associated with a processing load L1, and a neural network-based interference distribution compression method may be associated with a processing load L2. The evaluation between the codebook-based interference distribution compression method and the neural network-based compression method by UE115-a may be associated with a parameter β that can be communicated using control signaling 260. L1 * If the condition of β < L2 is satisfied, UE115-a may select to perform encoding according to a codebook-based interference distribution compression method (for example, configure the encoder 240 according to a codebook-based interference distribution compression method, encode the interference distribution according to a codebook-based interference distribution compression method, and indicate that the decoder 230 should be configured according to a codebook-based interference distribution compression method). L1 * If the condition of β < L2 is not satisfied, UE115-a may select encoding or decoding according to a neural network-based interference distribution compression method (for example, configure the encoder 240 according to an autoencoder, encode the interference distribution according to a neural network-based compression method, and indicate that the decoder 230 should be configured according to a neural network-based interference distribution compression method, and indicate that the decoder 230 should be configured according to an autoencoder).

[0147]

[0160] In some examples, the conditions for selecting one interference distribution compression scheme or another may be supported by an artificial neural network or a corresponding neural network configuration involved in the interference distribution compression scheme itself (e.g., an artificial neural network associated with the encoder 240, an artificial neural network associated with the decoder 230, or an artificial neural network associated with those configurations). For example, an artificial neural network used in the evaluation of an interference distribution compression scheme (e.g., in the compression scheme manager 250) for transmitting an interference information report 235 may take as input an autoencoder, an estimated channel (e.g., information regarding an estimate of signal propagation between the network entity 205-a and the UE 115-a), and an output of a regular or default interference distribution compression scheme (e.g., an output of a neural network-based interference distribution compression scheme). In some examples, an artificial neural network supporting such an evaluation may output a Boolean value indicating whether to fall back to a regular or default interference distribution compression scheme (e.g., whether to fall back to a codebook-based interference distribution compression scheme).

[0148]

[0161] In some examples, the network entity 205-a may configure parameters for interference prediction at a particular UE 115 based on the location of the UE 115 or based on known interference patterns. For example, the network entity 205-a may send control signaling 260 to the UE 115-a indicating an index of an interference distribution encoder from a defined table (e.g., stored in a memory of the UE 115-a).

[0149]

[0162] 3a illustrates an example of an encoding and decoding scheme 300 supporting compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. In some examples, the encoding and decoding scheme 300 may be implemented by or may implement aspects of the wireless communication system 100 or the wireless communication system 200. The encoding and decoding scheme 300 may include a UE 115-d, which may be an example of a UE 115 described herein. The encoding and decoding scheme 300 may also include a network entity 205-c, which may include all or some components of a base station 105 described herein.

[0150]

[0163] The UE 115-d may encode the past interference sequence 310-a, which represents the interference measured at the UE 115-d, via an encoder 240-a. The output 320-a of the encoder 240-a may be a compressed interference distribution. The UE 115-d may transmit the compressed interference distribution to the network entity 205-c. The network entity 205-c may include a decoder 230-a, which receives the compressed interference distribution as an input and decodes the compressed interference distribution according to a compression scheme. The output 330-a of the decoder 230-a may be an interference distribution.

[0151]

[0164] In some examples, the input 310-a may be a predicted interference sequence (e.g., the UE 115 may predict future interference based on interference measurements) and the output 330-a may correspondingly be a recovered predicted interference distribution.

[0152]

[0165] Utilizing the encoder 240-a and the decoder 230-a may enable the UE 115-d to report measured and / or predicted interference at the UE 115-d without transmitting the entire measured or predicted interference distribution, and may enable the network entity 205-c to receive and determine correlation characteristics of the interference at the UE 115-d, which the network entity 205-c may take into account when scheduling communications at the UE 115-d.

[0153]

[0166] 3b illustrates an example of an encoding and decoding scheme 305 supporting compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. In some examples, the encoding and decoding scheme 305 may be implemented by or may implement aspects of the wireless communication system 100 or the wireless communication system 200. The encoding and decoding scheme 305 may include a UE 115-e, which may be an example of a UE 115 described herein. The encoding and decoding scheme 305 may also include a network entity 205-d, which may include all or some components of a base station 105 described herein.

[0154]

[0167] The UE 115-e, via the encoder 240-b, may use a generative model to encode the past interference sequence 310-b, which represents the interference measured at the UE 115. In some examples, the encoder 240-b may take the past interference sequence 310-b and generate a mean vector (m) and a covariance matrix (V) that represent the distribution of latent random vectors ẑN(m,V), where m and V are the output of the encoder (m,V)=q ψ (x), where q ψdenotes an encoder 240-b parameterized by ψ, and N refers to a Gaussian (e.g., normal) distribution. The UE 115-e may transmit the encoder output 320-b, as well as the mean vector (m) and the covariance matrix (V). The network entity 205-d may include a decoder 230-b. The network entity 205-d may generate random samples according to the distribution {tilde over (x)}(,) and input z to the decoder 230-. The output 330-b of the decoder 230-b may represent the predicted interference samples x~q. θ (x│z), where q θ Let θ denote the decoder 230-b parameterized by θ. The decoder 230-b receives the compressed interference distribution as input and decodes the compressed interference distribution according to a compression scheme. The output 330-b of the decoder 230-b may be an interference sample sequence. The probability density function of the predicted interference sample x can be obtained as follows:

[0155]

number

[0156] This is done by sampling z multiple times,

[0157]

number

[0158] can be approximated as:

[0159]

[0168] 4 illustrates an example of an autoencoder 400 supporting compression and reconstruction of an interference distribution according to one or more aspects of the present disclosure. The autoencoder 400 may be implemented in the network entity 205-e and / or in the UE 115, or both, as described with reference to FIGS.

[0160]

[0169] The autoencoder 400 includes an encoder 240-c and a decoder 230-c, each of which may include multiple layers (440 and 430, respectively). The encoder 240-c may be implemented in the UE 115-f. The encoder 240-c may receive an input 405 at a first layer 440-a, and the encoder 240-c may compress the input received at each successive layer 440-b and 440-c. The input 405 may be an interference distribution. Each layer 440 of the encoder 240-c may include multiple nodes 445. The encoder 240-c may reduce the size of the interference distribution at each layer 440 of the encoder to a smaller representation. For example, each successive layer 440 of the encoder 240-c may include fewer nodes 445. The code 415 may be an output of the encoder 240-c. The code 415 may be transmitted to the decoder 230-c. The decoder 230-c may be implemented in a network entity 205-d. For example, the code 415 may be transmitted from the UE 115, which may include the encoder 240-c, to a network entity 205-e, which may include the decoder 230-c.

[0161]

[0170] The decoder 230-c may receive the code 415 at a first layer 430-c and recover the original data or an estimate of the original data via passing the data through successive layers 430-c, 430-b, and 430-a. Each layer 430 of the decoder 230-c may include multiple nodes 435. Each successive layer 430 of the decoder 230-c may include more nodes. The output 410 of the decoder 230-c may be the recovered data (e.g., an interference distribution).

[0162]

[0171] The encoder 240-c and decoder 230-c may be trained to minimize error and resource overhead in compressing and recovering the interference distribution. For example, the code size may represent a trade-off between overhead size and interference distribution reconstruction error. The encoder 240-c and decoder 230-c may adjust the code size, the number of layers, the number of nodes per layer, and / or a loss function based on comparing the input 405 to the output 410. For example, the encoder 240-c and decoder 230-c may execute a control routine periodically or aperiodically (e.g., the encoder 240-c may compress and transmit a data set that is known to the decoder 230-c) so that the encoder 240-c and decoder 230-c may determine an error in reconstruction and adjust parameters of the encoder 240-c and decoder 230-c. In some examples, the UE 115 may periodically or aperiodically transmit uncompressed interference distribution data to the network entity 205-e so that the encoder 240-c and decoder 230-c may determine an error in the reconstruction of the corresponding compressed interference distribution data and adjust parameters of the encoder 240-c and decoder 230-c.

[0163]

[0172] 5 illustrates an example of a machine learning process 500 that supports compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. The machine learning process 500 may be implemented in the base station 105 (e.g., in a network entity), or in the UE 115, or both, as described with reference to FIGS.

[0164]

[0173] The machine learning process 500 may include a machine learning algorithm 510. As illustrated, the machine learning algorithm 510 may be an example of an artificial neural network, such as a feed-forward (FF) or deep feed-forward (DFF) neural network, a recurrent neural network (RNN), a long / short-term memory (LSTM) neural network, or any other type of artificial neural network. However, any other machine learning algorithm may be supported. For example, the machine learning algorithm 510 may implement a nearest neighbor algorithm, a linear regression algorithm, a naive Bayes algorithm, a random forest algorithm, or any other machine learning algorithm. Furthermore, the machine learning process 500 may involve supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, or any combination thereof.

[0165]

[0174] The machine learning algorithm 510 may include an input layer 515, one or more hidden layers 520, and an output layer 525. In a fully connected neural network with one hidden layer 520, each hidden layer node 535 may receive values ​​from each input layer node 530 as input, where each input may be weighted. These neural network weights may be based on a cost function that is revised during training of the machine learning algorithm 510. Similarly, each output layer node 540 may receive values ​​from each hidden layer node 535 as input, where each input is weighted. If post-deployment training (e.g., online training) is supported, memory may be allocated to store errors and / or gradients for reversing the matrix multiplication. These errors and / or gradients may support updating the machine learning algorithm 510 based on the output feedback. Training the machine learning algorithm 510 may support the computation of weights (e.g., connecting input layer nodes 530 to hidden layer nodes 535, and connecting hidden layer nodes 535 to output layer nodes 540) to map input patterns to desired output results. This training may result in a device-specific machine learning algorithm 510 based on historical application data and data transfers for a particular base station 105 or UE 115.

[0166]

[0175] In some examples, the input values ​​505 may be sent to the machine learning algorithm 510 for processing. In some examples, pre-processing may be performed according to a series of operations on the input values ​​505 so that the input values ​​505 may be in a format compatible with the machine learning algorithm 510. The input values ​​505 may be converted into a set of k input layer nodes 530 in the input layer 515. In some cases, different measurements may be input at different input layer nodes 530 of the input layer 515. If the number of input layer nodes 530 exceeds the number of inputs corresponding to the input values ​​505, some input layer nodes 530 may be assigned default values ​​(e.g., values ​​of 0). As illustrated, the input layer 515 may include three input layer nodes 530-a, 530-b, and 530-c. However, it should be understood that the input layer 515 may include any number of input layer nodes 530 (e.g., 20 input nodes).

[0167]

[0176] The machine learning algorithm 510 may convert the input layer 515 into a hidden layer 520 based on the number of input-hidden weights between the k input layer nodes 530 and the n hidden layer nodes 535. The machine learning algorithm 510 may include any number of hidden layers 520 as an intermediate step between the input layer 515 and the output layer 525. Additionally or alternatively, each hidden layer 520 may include any number of nodes. For example, as illustrated, the hidden layer 520 may include four hidden layer nodes 535-a, 535-b, 535-c, and 535-d. However, it should be understood that the hidden layer 520 may include any number of hidden layer nodes 535 (e.g., 10 input nodes). In a fully connected neural network, each node in a layer may be based on each node in the previous layer. For example, the value of hidden layer node 535-a may be based on input layer nodes 530-a, 530-b, and 530-c (eg, with different weights applied to each node value).

[0168]

[0177] The machine learning algorithm 510 may determine values ​​for output layer nodes 540 of an output layer 525 following one or more hidden layers 520. For example, the machine learning algorithm 510 may convert the hidden layer 520 to an output layer 525 based on the number of hidden-output weights between the n hidden layer nodes 535 and the m output layer nodes 540. In some cases, n=m. Each output layer node 540 may correspond to a different output value 545 of the machine learning algorithm 510. As illustrated, the machine learning algorithm 510 may include three output layer nodes 540-a, 540-b, and 540-c supporting three different threshold values. However, it should be understood that the output layer 525 may include any number of output layer nodes 540. In some examples, post-processing may be performed on the output values ​​545 according to a sequence of operations such that the output values ​​545 may be in a format suitable for reporting the output values ​​545.

[0169]

[0178] As described herein, a fully connected NN includes a series of fully connected layers that connect all neurons in one layer to all neurons in the other layers. A batch normalized NN includes a normalization step that fixes the mean and variance of each layer of the neural network's input. A dropout NN may ignore randomly selected nodes during the training of a neural network. For example, at each training stage, individual nodes are dropped from the net with probability 1-p or kept with probability p. A convolutional NN is also referred to as a shift-invariant or space-invariant artificial neural network (SIANN). A convolutional neural network includes an input layer, a hidden layer, and an output layer. In a feedforward neural network, the intermediate layers may be referred to as hidden because their inputs and outputs are masked by an activation function and a final convolution. In a convolutional neural network, the hidden layer includes a layer that performs a convolution. Typically, the hidden layer includes a layer that performs a dot product of a convolution kernel and the input matrix of the layer. This product is usually a Frobenius inner product, and the activation function is typically a ReLU. As the convolution kernel slides along the input matrix of a layer, the convolution operation produces a feature map, which contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers. ReLu NN is a neural network with its argument f(x)=x + = max(0,x), where x is the input to the neuron. Residual NNs may utilize skip connections to jump between layers of the NN and may be implemented using two or three layers skipping with nonlinearities (e.g., ReLus) and batch normalization in between.

[0170]

[0179] FIG. 6 illustrates an example of a process flow 600 supporting compression and reconstruction of interference distributions according to one or more aspects of the disclosure. In some examples, the process flow 600 may be implemented by or may implement aspects of the wireless communication system 100 or the wireless communication system 200. The process flow 600 may include a UE 115-g, which may be an example of a UE 115 described herein. The process flow 600 may also include a network entity 205-f, which may include all or some components of a base station 105 described herein. In the following description of the process flow 600, operations between the network entity 205-f and the UE 115-g may be transmitted in a different order than the example order shown, or operations performed by the network entity 205-f and the UE 115-g may be performed in a different order or at different times. Some operations may also be omitted from the process flow 600, and other operations may be added to the process flow 600.

[0171]

[0180] At 605, the UE 115-g may send, to the network entity 205-f, an indication of the capability of the UE 115-g to encode interference information.

[0172]

[0181] At 610, the network entity 205-f may transmit to the UE 115-g control signaling associated with the interference information report. In some examples, the control signaling may indicate a coding configuration for encoding the interference information. In some examples, the control signaling may indicate an index associated with the selected coding configuration, each coding configuration of the set of coding configurations associated with a respective index of the set of indexes. In some examples, the network entity 205-f may select the coding configuration based on the capability message. In some examples, the coding configuration may be an autoencoder or an artificial neural network.

[0173]

[0182] In some examples, the control signaling may indicate one or more parameters associated with a compression scheme used by the encoding configuration. In some examples, the one or more parameters include a code size, a number of layers, a number of nodes per layer, a loss function, or a combination thereof. In some examples, the control signaling may indicate an input format for encoding interference information at the UE 115-g.

[0174]

[0183] In some examples, the control signaling may indicate an interference measurement resource for measuring interference at the UE 115-g. In some examples, the control signaling may indicate one or more parameters associated with measuring interference at the UE 115-g over the indicated interference measurement resource. In some examples, the one or more parameters include frequency granularity, time granularity, spatial granularity, or a combination thereof.

[0175]

[0184] At 615, the UE 115-g may measure interference at the UE across the set of interference measurement resources. In some cases, the UE 115-g may measure interference plus noise at the UE across the set of interference measurement resources.

[0176]

[0185] At 620, the UE 115-g may encode interference information representing a distribution of interference at the UE based on the measured interference across the set of interference measurement resources according to a compression scheme. In some examples, the UE 115-g may encode the interference information via generating a mean vector and a covariance matrix of latent random variables representing a distribution of interference at the UE across the set of interference measurement resources, the set of interference measurement resources including two or more sets of time resources, frequency resources, or spatial resources. In some cases, the distribution of interference may be a distribution of interference plus noise across the set of interference measurement resources.

[0177]

[0186] At 625, the UE 115-g may transmit the interference information encoded according to the compression scheme to the network entity 205-f. At 630, the network entity 205-f may decode the interference information according to the compression scheme. In some examples, the UE 115-g may transmit the interference information encoded according to the compression scheme in a CSF report. At 635, the network entity 205-f may transmit scheduling information for communication at the UE 115-g based on the decoded interference information.

[0178]

[0187] In some examples, the UE 115-g may determine one or more model parameters associated with the compression scheme using an artificial NN associated with the compression scheme and transmit the one or more model parameters to the network entity 205-f. The network entity 205-f may determine and transmit (e.g., via a configuration message) one or more parameters associated with the compression scheme based on the one or more model parameters to the UE 115-g.

[0179]

[0188] In some examples, the network entity 205-f may determine one or more model parameters associated with the compression scheme using an artificial NN associated with the compression scheme. The network entity 205-f may transmit the one or more parameters associated with the compression scheme to the UE 115-g (e.g., via a configuration message) based on the one or more model parameters.

[0180]

[0189] 7 illustrates a block diagram 700 of a device 705 supporting compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. The device 705 may be an example of an aspect of a UE 115 described herein. The device 705 may include a receiver 710, a transmitter 715, and a communications manager 720. The device 705 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0181]

[0190] The receiver 710 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various traffic channels (e.g., control channels, data channels, traffic channels related to compression and reconstruction of interference distributions). The information may be passed to other components of the device 705. The receiver 710 may utilize a single antenna or a set of multiple antennas.

[0182]

[0191] The transmitter 715 may provide a means for transmitting signals generated by other components of the device 705. For example, the transmitter 715 may transmit information such as packets associated with various traffic channels (e.g., control channels, data channels, traffic channels related to compression and reconstruction of interference distributions), user data, control information, or any combination thereof. In some examples, the transmitter 715 may be collocated with the receiver 710 within a transceiver module. The transmitter 715 may utilize a single antenna or a set of multiple antennas.

[0183]

[0192] The communications manager 720, the receiver 710, the transmitter 715, or various combinations or components thereof may be examples of means for performing various aspects of the compression and reconstruction of interference distributions as described herein. For example, the communications manager 720, the receiver 710, the transmitter 715, or various combinations or components thereof may support a method for implementing one or more of the functions described herein.

[0184]

[0193] In some examples, the communications manager 720, the receiver 710, the transmitter 715, or various combinations or components thereof may be implemented in hardware (e.g., in a communications management circuit). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting a means for performing the functions described in this disclosure. In some examples, the processor and a memory coupled to the processor may be configured to perform one or more of the functions described herein (e.g., by the processor executing instructions stored in the memory).

[0185]

[0194] Additionally or alternatively, in some embodiments, the communications manager 720, the receiver 710, the transmitter 715, or various combinations or components thereof may be implemented in code executed by a processor (e.g., as communications management software or firmware). If implemented in code executed by a processor, the functions of the communications manager 720, the receiver 710, the transmitter 715, or various combinations or components thereof may be performed by a general purpose processor (e.g., configured as or otherwise supporting a means for performing the functions described in this disclosure), a DSP, a central processing unit (CPU), an ASIC, an FPGA, or any combination of these or other programmable logic devices.

[0186]

[0195] In some examples, the communications manager 720 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the receiver 710, the transmitter 715, or both. For example, the communications manager 720 may receive information from the receiver 710, transmit information to the transmitter 715, or may be integrated in combination with the receiver 710, the transmitter 715, or both to receive information, transmit information, or perform various other operations described herein.

[0187]

[0196] The communications manager 720 may support wireless communications at the UE according to embodiments disclosed herein. For example, the communications manager 720 may be configured as or otherwise support a means for measuring interference at the UE across a set of interference measurement resources. The communications manager 720 may be configured as or otherwise support a means for encoding interference information representing a distribution of interference at the UE based on the measured interference across the set of interference measurement resources according to a compression scheme. The communications manager 720 may be configured as or otherwise support a means for transmitting the interference information encoded according to the compression scheme to a first network entity.

[0188]

[0197] By including or configuring the communications manager 720 according to examples described herein, the device 705 (e.g., a processor controlling or otherwise coupled to the receiver 710, the transmitter 715, the communications manager 720, or a combination thereof) may support techniques for more efficient utilization of communications resources through providing dynamic measurement and reporting of interference information at the UE.

[0189]

[0198] 8 illustrates a block diagram 800 of a device 805 that supports compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. The device 805 may be an example of an aspect of a device 705 or a UE 115 described herein. The device 805 may include a receiver 810, a transmitter 815, and a communications manager 820. The device 805 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0190]

[0199] The receiver 810 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various traffic channels (e.g., control channels, data channels, traffic channels related to compression and reconstruction of interference distributions). The information may be passed to other components of the device 805. The receiver 810 may utilize a single antenna or a set of multiple antennas.

[0191]

[0200] The transmitter 815 may provide a means for transmitting signals generated by other components of the device 805. For example, the transmitter 815 may transmit information such as packets associated with various traffic channels (e.g., control channels, data channels, traffic channels related to compression and reconstruction of interference distributions), user data, control information, or any combination thereof. In some examples, the transmitter 815 may be collocated with the receiver 810 within a transceiver module. The transmitter 815 may utilize a single antenna or a set of multiple antennas.

[0192]

[0201] The device 805, or various components thereof, may be an example of a means for performing various aspects of compression and reconstruction of interference distributions as described herein. For example, the communications manager 820 may include an interference measurement manager 825, a compression manager 830, an interference reporting manager 835, or any combination thereof. The communications manager 820 may be an example of an aspect of the communications manager 720 as described herein. In some examples, the communications manager 820 or various components thereof may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the receiver 810, the transmitter 815, or both. For example, the communications manager 820 may receive information from the receiver 810, transmit information to the transmitter 815, or may be integrated in combination with the receiver 810, the transmitter 815, or both to receive information, transmit information, or perform various other operations as described herein.

[0193]

[0202] The communications manager 820 may support wireless communications at the UE according to embodiments disclosed herein. The interference measurement manager 825 may be configured as or otherwise support a means for measuring interference at the UE across a set of interference measurement resources. The compression manager 830 may be configured as or otherwise support a means for encoding interference information representing a distribution of interference at the UE based on the measured interference across the set of interference measurement resources according to a compression scheme. The interference report manager 835 may be configured as or otherwise support a means for transmitting the interference information encoded according to a compression scheme to a first network entity.

[0194]

[0203] 9 illustrates a block diagram 900 of a communications manager 920 supporting compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. Communications manager 920 may be an example of aspects of communications manager 720, communications manager 820, or both, as described herein. Communications manager 920, or various components thereof, may be an example of a means for performing various aspects of compression and reconstruction of interference distributions as described herein. For example, communications manager 920 may include an interference measurement manager 925, a compression manager 930, an interference reporting manager 935, a coding configuration manager 940, a CSF manager 945, a neural network manager 950, a learned parameters manager 955, a UE coding capabilities manager 960, or any combination thereof. Each of these components may communicate directly or indirectly with each other (e.g., via one or more buses).

[0195]

[0204] The communications manager 920 may support wireless communications at the UE according to embodiments disclosed herein. The interference measurement manager 925 may be configured as or otherwise support a means for measuring interference at the UE across a set of interference measurement resources. The compression manager 930 may be configured as or otherwise support a means for encoding interference information representing a distribution of interference at the UE based on the measured interference across the set of interference measurement resources according to a compression scheme. The interference report manager 935 may be configured as or otherwise support a means for transmitting the interference information encoded according to a compression scheme to a first network entity.

[0196]

[0205] In some examples, the interference at the UE may be interference plus noise, and the distribution of the interference may be a distribution of the interference plus noise across the set of interference measurement resources.

[0197]

[0206] In some examples, the distribution of interference at the UE may include a probability mass function for a set of resources in time, frequency, and / or space.

[0198]

[0207] In some examples, the set of resources in time, frequency, and / or space includes a set of interference measurement resources.

[0199]

[0208] In some examples, the set of resources in time, frequency, and / or space includes resources prior to the set of interference measurement resources, and the distribution of interference at the UE is based on the measured interference across the set of interference measurement resources.

[0200]

[0209] In some examples, the set of resources in time, frequency, and / or space includes resources that are later than the set of interference measurement resources, and a distribution of interference at the UE is predicted based at least in part on the measured interference across the set of interference measurement resources.

[0201]

[0210] In some examples, to encode the interference information, the compression manager 930 may be configured as or otherwise support a means for generating a condensed, estimated or predicted interference distribution over a set of interference measurement resources.

[0202]

[0211] In some examples, the compression scheme includes a codeword-based compression scheme or an artificial neural network-based compression scheme.

[0203]

[0212] In some examples, to encode the interference information, the compression manager 930 may be configured as or otherwise support a means for generating a mean vector and a covariance matrix of latent random variables representing the distribution of interference at the UE across a set of interference measurement resources, the set of interference measurement resources including two or more sets of time, frequency, or spatial resources.

[0204]

[0213] In some examples, the encoding configuration manager 940 may be configured as or otherwise support a means for receiving an indication of a encoding configuration for encoding interference information from the first network entity or one or more second network entities associated with the first network entity.

[0205]

[0214] In some examples, the UE encoding capabilities manager 960 may be configured as or otherwise support a means for transmitting, to the first network entity or one or more second network entities associated with the first network entity, an indication of the UE's capability to encode interference information. In some examples, the encoding configuration manager 940 may be configured as or otherwise support a means for receiving, in response to transmitting, from the first network entity or one or more second network entities associated with the first network entity, an indication of an index associated with the encoding configuration, where a set of encoding configurations including the encoding configuration is associated with a set of indexes including the index.

[0206]

[0215] In some examples, the encoding configuration includes configuration for one of an autoencoder or an artificial neural network.

[0207]

[0216] In some examples, the coding configuration manager 940 may be configured as or otherwise support a means for selecting one coding configuration from a set of coding configurations for encoding interference information, each coding configuration of the set of coding configurations being associated with a respective index of a set of indexes. In some examples, the coding configuration manager 940 may be configured as or otherwise support a means for transmitting an indication of one index of the set of indexes associated with the selected coding configuration from the first network entity or one or more second network entities associated with the first network entity.

[0208]

[0217] In some examples, the encoding configuration manager 940 may be configured as or otherwise support a means for receiving an indication of one or more parameters associated with a compression scheme from the first network entity or one or more second network entities associated with the first network entity.

[0209]

[0218] In some examples, the one or more parameters associated with the compression scheme include a code size, a number of layers, a number of nodes per layer, a loss function, or a combination thereof.

[0210]

[0219] In some examples, the interference measurement manager 925 may be configured as or otherwise support a means for receiving an indication of a set of interference measurement resources from the first network entity or one or more second network entities associated with the first network entity.

[0211]

[0220] In some examples, the interference measurement manager 925 may be configured as or otherwise support a means for receiving, from the first network entity or one or more second network entities associated with the first network entity, an indication of one or more parameters associated with measuring interference at a UE across a set of interference measurement resources.

[0212]

[0221] In some examples, the one or more parameters associated with measuring interference at a UE across a set of interference measurement resources include frequency granularity, time granularity, spatial granularity, or a combination thereof.

[0213]

[0222] In some examples, the interference reporting manager 935 may be configured as or otherwise support a means for receiving, from the first network entity or one or more second network entities associated with the first network entity, an indication of an input format for encoding interference information representing a distribution of interference at the UE.

[0214]

[0223] In some examples, the CSF manager 945 may be configured as or otherwise support a means for transmitting channel condition feedback reports including encoded interference information encoded according to a compression scheme.

[0215]

[0224] In some examples, the neural network manager 950 may be configured as or otherwise support a means for determining one or more model parameters associated with the compression scheme using an artificial neural network associated with the compression scheme. In some examples, the learned parameter manager 955 may be configured as or otherwise support a means for transmitting one or more model parameters to the first network entity or to one or more second network entities associated with the first network entity.

[0216]

[0225] In some examples, the encoding configuration manager 940 may be configured as or otherwise support a means for receiving one or more parameters associated with a compression scheme based on transmitting one or more model parameters from a first network entity or one or more second network entities associated with the first network entity.

[0217]

[0226] 10 illustrates a diagram of a system 1000 including a device 1005 supporting compression and reconstruction of interference distributions according to one or more aspects of the disclosure. The device 1005 may be an example of or may include a component of a device 705, a device 805, or a UE 115 described herein. The device 1005 may wirelessly communicate with one or more network entities 205, a UE 115, or any combination thereof. The device 1005 may include components for two-way voice and data communication, including components for transmitting and receiving communications, such as a communications manager 1020, an input / output (I / O) controller 1010, a transceiver 1015, an antenna 1025, a memory 1030, code 1035, and a processor 1040. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., a bus 1045).

[0218]

[0227] The I / O controller 1010 may manage input and output signals for the device 1005. The I / O controller 1010 may also manage peripheral devices that are not integrated with the device 1005. In some cases, the I / O controller 1010 may represent a physical connection or port to an external peripheral device. In some cases, the I / O controller 1010 may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS / 2®, UNIX®, LINUX®, or another known operating system. Additionally or alternatively, the I / O controller 1010 may represent or interact with a modem, keyboard, mouse, touch screen, or similar device. In some cases, the I / O controller 1010 may be implemented as part of a processor, such as the processor 1040. In some cases, a user may interact with the device 1005 through the I / O controller 1010 or through hardware components controlled by the I / O controller 1010.

[0219]

[0228] In some cases, the device 1005 may include a single antenna 1025. However, in some other cases, the device 1005 may have two or more antennas 1025, which may be capable of simultaneously transmitting or receiving multiple wireless transmissions. The transceiver 1015 may communicate bidirectionally via one or more antennas 1025, a wired link, or a wireless link as described herein. For example, the transceiver 1015 may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. The transceiver 1015 may also include a modem for modulating packets and providing the modulated packets to the one or more antennas 1025 for transmission, and for demodulating packets received from the one or more antennas 1025. The transceiver 1015, or the transceiver 1015 and one or more antennas 1025, may be an example of the transmitter 715, transmitter 815, receiver 710, receiver 810, or any combination or component thereof described herein.

[0220]

[0229] The memory 1030 may include random access memory (RAM) and read-only memory (ROM). The memory 1030 may store computer-readable computer-executable code 1035 including instructions that, when executed by the processor 1040, cause the device 1005 to perform various functions described herein. The code 1035 may be stored in a non-transitory computer-readable medium, such as a system memory or another type of memory. In some cases, the code 1035 may not be directly executable by the processor 1040, but may (e.g., when compiled and executed) cause a computer to perform functions described herein. In some cases, the memory 1030 may include a basic I / O system (BIOS), which may control basic hardware or software operations, such as interactions with peripheral components or devices, among others.

[0221]

[0230] The processor 1040 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor 1040 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated with the processor 1040. The processor 1040 may be configured to execute computer-readable instructions stored in a memory (e.g., memory 1030) to cause the device 1005 to perform various functions (e.g., functions or tasks supporting compression and reconstruction of interference distributions). For example, the device 1005 or a component of the device 1005 may include the processor 1040 and the memory 1030 coupled or coupled to the processor 1040, where the processor 1040 and the memory 1030 are configured to perform various functions described herein.

[0222]

[0231] The communications manager 1020 may support wireless communications at the UE according to embodiments disclosed herein. For example, the communications manager 1020 may be configured as or otherwise support a means for measuring interference at the UE across a set of interference measurement resources. The communications manager 1020 may be configured as or otherwise support a means for encoding interference information representing a distribution of interference at the UE based on the measured interference across the set of interference measurement resources according to a compression scheme. The communications manager 1020 may be configured as or otherwise support a means for transmitting the interference information encoded according to the compression scheme to a first network entity.

[0223]

[0232] By including or configuring a communications manager 1020 in accordance with examples described herein, the device 1005 may support techniques for improved communications reliability, more efficient utilization of communications resources, and improved inter-device coordination through providing dynamic measurement and reporting of interference information at the UE and scheduling of communications that takes into account the interference information at the UE.

[0224]

[0233] In some examples, the communications manager 1020 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the transceiver 1015, one or more antennas 1025, or any combination thereof. Although the communications manager 1020 is shown as a separate component, in some examples, one or more functions described with reference to the communications manager 1020 may be supported or performed by the processor 1040, the memory 1030, the code 1035, or any combination thereof. For example, the code 1035 may include instructions executable by the processor 1040 to cause the device 1005 to perform various aspects of the compression and reconstruction of interference distributions described herein, or the processor 1040 and the memory 1030 may be otherwise configured to perform or support such operations.

[0225]

[0234] 11 illustrates a block diagram 1100 of a device 1105 supporting compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. The device 1105 may be an example of an aspect of a base station 105 or a network entity as described herein. The device 1105 may include a receiver 1110, a transmitter 1115, and a communications manager 1120. The device 1105 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0226]

[0235] The receiver 1110 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various traffic channels (e.g., control channels, data channels, traffic channels related to compression and reconstruction of interference distributions). The information may be passed to other components of the device 1105. The receiver 1110 may utilize a single antenna or a set of multiple antennas.

[0227]

[0236] The transmitter 1115 may provide a means for transmitting signals generated by other components of the device 1105. For example, the transmitter 1115 may transmit information such as packets associated with various traffic channels (e.g., control channels, data channels, traffic channels related to compression and reconstruction of interference distributions), user data, control information, or any combination thereof. In some examples, the transmitter 1115 may be collocated with the receiver 1110 within a transceiver module. The transmitter 1115 may utilize a single antenna or a set of multiple antennas.

[0228]

[0237] The communications manager 1120, the receiver 1110, the transmitter 1115, or various combinations or components thereof may be examples of means for performing various aspects of interference distribution compression and reconstruction as described herein. For example, the communications manager 1120, the receiver 1110, the transmitter 1115, or various combinations or components thereof may support a method for performing one or more of the functions described herein.

[0229]

[0238] In some examples, the communications manager 1120, the receiver 1110, the transmitter 1115, or various combinations or components thereof may be implemented in hardware (e.g., in a communications management circuit). The hardware may include a processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting a means for performing the functions described in this disclosure. In some examples, the processor and a memory coupled to the processor may be configured to perform one or more of the functions described herein (e.g., by the processor executing instructions stored in the memory).

[0230]

[0239] Additionally or alternatively, in some embodiments, the communications manager 1120, the receiver 1110, the transmitter 1115, or various combinations or components thereof may be implemented in code executed by a processor (e.g., as communications management software or firmware). If implemented in code executed by a processor, the functionality of the communications manager 1120, the receiver 1110, the transmitter 1115, or various combinations or components thereof may be performed by a general purpose processor, a DSP, a CPU, an ASIC, an FPGA, or any combination of these or other programmable logic devices (e.g., configured as or otherwise supporting a means for performing the functions described in this disclosure).

[0231]

[0240] In some examples, the communications manager 1120 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the receiver 1110, the transmitter 1115, or both. For example, the communications manager 1120 may receive information from the receiver 1110, transmit information to the transmitter 1115, or may be integrated in combination with the receiver 1110, the transmitter 1115, or both to receive information, transmit information, or perform various other operations described herein.

[0232]

[0241] The communications manager 1120 may support wireless communications at the base station according to examples disclosed herein. For example, the communications manager 1120 may be configured as or otherwise support a means for obtaining encoded interference information representative of a distribution of interference. The communications manager 1120 may be configured as or otherwise support a means for decoding the encoded interference information according to a compression scheme to output the decoded interference information.

[0233]

[0242] By including or configuring the communications manager 1120 according to examples described herein, the device 1105 (e.g., a processor controlling or otherwise coupled to the receiver 1110, the transmitter 1115, the communications manager 1120, or a combination thereof) may support techniques for more efficient utilization of communications resources through providing dynamic measurement and reporting of interference information at the UE.

[0234]

[0243] 12 illustrates a block diagram 1200 of a device 1205 supporting compression and reconstruction of an interference distribution in accordance with one or more aspects of the present disclosure. The device 1205 may be an example of an aspect of a device 1105, a base station 105, or a network entity as described herein. The device 1205 may include a receiver 1210, a transmitter 1215, and a communications manager 1220. The device 1205 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0235]

[0244] The receiver 1210 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various traffic channels (e.g., control channels, data channels, traffic channels related to compression and reconstruction of interference distributions). The information may be passed to other components of the device 1205. The receiver 1210 may utilize a single antenna or a set of multiple antennas.

[0236]

[0245] The transmitter 1215 may provide a means for transmitting signals generated by other components of the device 1205. For example, the transmitter 1215 may transmit information such as packets associated with various traffic channels (e.g., control channels, data channels, traffic channels related to compression and reconstruction of interference distributions), user data, control information, or any combination thereof. In some examples, the transmitter 1215 may be collocated with the receiver 1210 within a transceiver module. The transmitter 1215 may utilize a single antenna or a set of multiple antennas.

[0237]

[0246] The device 1205, or various components thereof, may be an example of a means for performing various aspects of compression and reconstruction of interference distributions as described herein. For example, the communications manager 1220 may include an interference report manager 1230, an interference report decoding manager 1225, or any combination thereof. The communications manager 1220 may be an example of an aspect of the communications manager 1120 as described herein. In some examples, the communications manager 1220, or various components thereof, may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the receiver 1210, the transmitter 1215, or both. For example, the communications manager 1220 may receive information from the receiver 1210 and transmit information to the transmitter 1215, or may be integrated in combination with the receiver 1210, the transmitter 1215, or both to receive information, transmit information, or perform various other operations as described herein.

[0238]

[0247] The communications manager 1220 may support wireless communications at the base station according to examples disclosed herein. The interference report manager 1225 may be configured as or otherwise support a means for obtaining encoded interference information representative of a distribution of interference. The interference report decoding manager 1230 may be configured as or otherwise support a means for decoding the encoded interference information according to a compression scheme to output the decoded interference information.

[0239]

[0248] 13 illustrates a block diagram 1300 of a communications manager 1320 supporting compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. The communications manager 1320 may be an example of aspects of the communications manager 1120, the communications manager 1220, or both described herein. The communications manager 1320, or various components thereof, may be an example of a means for performing various aspects of compression and reconstruction of interference distributions as described herein. For example, the communications manager 1320 may include an interference report manager 1325, an interference report decoder 1330, a scheduling manager 1335, a coding configuration manager 1340, an interference measurement configuration manager 1345, a CSF manager 1350, a neural network manager 1355, a learned parameter manager 1360, a UE coding capabilities manager 1365, or any combination thereof. Each of these components may communicate directly or indirectly with each other (e.g., via one or more buses).

[0240]

[0249] The communications manager 1320 may support wireless communications in the base station according to examples disclosed herein. The interference report manager 1325 may be configured as or otherwise support a means for obtaining encoded interference information representative of a distribution of interference across a set of interference measurement resources. The interference report decoding manager 1330 may be configured as or otherwise support a means for decoding the encoded interference information according to a compression scheme to output the decoded interference information.

[0241]

[0250] In some examples, the distribution of interference may be a distribution of interference plus noise across a set of interference measurement resources.

[0242]

[0251] In some examples, the scheduling manager 1335 may be configured or otherwise support as a means for outputting scheduling information for communication at the UE based on the decoded interference information.

[0243]

[0252] In some examples, the coded interference information includes a mean vector and a covariance matrix of latent random variables that represent a distribution of interference at the UE across the set of interference measurement resources. The interference report decoding manager 1330 may be configured as or otherwise support a means for generating samples based on the mean vector and the covariance matrix, and for decoding the coded interference information based at least in part on the samples.

[0244]

[0253] In some examples, the coding configuration manager 1340 may be configured as or otherwise support a means for outputting an indication of a coding configuration for coding interference information at the UE.

[0245]

[0254] In some examples, the UE coding capabilities manager 1365 may be configured as or otherwise support a means for obtaining an indication of a capability of the UE to code interference information. In some examples, the coding configuration manager 1340 may be configured as or otherwise support a means for outputting an indication of an index associated with a coding configuration based on the indication of a capability of the UE to code interference information, where a set of coding configurations including the coding configuration is associated with a set of indexes including the index.

[0246]

[0255] In some examples, the encoding configuration includes configuration for one of an autoencoder or an artificial neural network.

[0247]

[0256] In some examples, the encoding configuration manager 1340 may be configured as or otherwise support a means for obtaining an indication of one index of a set of indexes associated with a selected encoding configuration, each encoding configuration of the set of encoding configurations being associated with a respective index of the set of indexes.

[0248]

[0257] In some examples, the encoding configuration manager 1340 may be configured as or otherwise support a means for outputting an indication of one or more parameters associated with a compression scheme.

[0249]

[0258] In some examples, the one or more parameters associated with the compression scheme include a code size, a number of layers, a number of nodes per layer, a loss function, or a combination thereof.

[0250]

[0259] In some examples, the interference measurement configuration manager 1345 may be configured as or otherwise support a means for outputting an indication of one or more parameters associated with measuring interference across a set of interference measurement resources.

[0251]

[0260] In some examples, the one or more parameters associated with measuring interference across a set of interference measurement resources include frequency granularity, time granularity, spatial granularity, or a combination thereof.

[0252]

[0261] In some examples, the encoding configuration manager 1340 may be configured as or otherwise support a means for outputting an indication of an input format for encoding interference information representing a distribution of interference.

[0253]

[0262] In some examples, the CSF manager 1350 may be configured as or otherwise support a means for obtaining channel condition feedback reports including encoded interference information encoded according to a compression scheme.

[0254]

[0263] In some examples, the neural network manager 1355 may be configured as or otherwise support a means for determining one or more model parameters associated with the compression scheme using an artificial neural network associated with the compression scheme. In some examples, the encoding configuration manager 1340 may be configured as or otherwise support a means for outputting one or more parameters associated with the compression scheme based on the one or more model parameters.

[0255]

[0264] In some examples, the learned parameter manager 1360 may be configured as or otherwise support a means for obtaining one or more model parameters associated with a compression scheme. In some examples, the encoding configuration manager 1340 may be configured as or otherwise support a means for outputting one or more parameters associated with a compression scheme based on the one or more model parameters.

[0256]

[0265] FIG. 14 illustrates a diagram of a system 1400 including a device 1405 supporting compression and reconstruction of interference distributions according to one or more aspects of the disclosure. The device 1405 may be or may include an example of a device 1105, device 1205, network entity, or base station 105 component described herein. The device 1405 may wirelessly communicate with one or more base stations 105, UEs 115, or any combination thereof. The device 1405 may include components for two-way voice and data communication, including components for transmitting and receiving communications, such as a communications manager 1420, a network communications manager 1410, a transceiver 1415, an antenna 1425, a memory 1430, code 1435, a processor 1440, and an inter-station communications manager 1445. These components may electronically communicate or be otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., a bus 1450).

[0257]

[0266] The network communications manager 1410 may manage communications with the core network 130 (e.g., over one or more wired backhaul links). For example, the network communications manager 1410 may manage the transfer of data communications for client devices, such as one or more UEs 115.

[0258]

[0267] In some cases, the device 1405 may include a single antenna 1425. However, in some other cases, the device 1405 may have two or more antennas 1425, which may be capable of simultaneously transmitting or receiving multiple wireless transmissions. The transceiver 1415 may communicate bidirectionally via one or more antennas 1425, a wired link, or a wireless link as described herein. For example, the transceiver 1415 may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. The transceiver 1415 may also include a modem for modulating packets and providing the modulated packets to the one or more antennas 1425 for transmission, and for demodulating packets received from the one or more antennas 1425. The transceiver 1415, or the transceiver 1415 and the one or more antennas 1425, may be an example of the transmitter 1115, the transmitter 1215, the receiver 1110, the receiver 1210, or any combination or component thereof described herein.

[0259]

[0268] The memory 1430 may include RAM and ROM. The memory 1430 may store computer-readable computer-executable code 1435 including instructions that, when executed by the processor 1440, cause the device 1405 to perform various functions described herein. The code 1435 may be stored in a non-transitory computer-readable medium, such as a system memory or another type of memory. In some cases, the code 1435 may not be directly executable by the processor 1440, but may (e.g., when compiled and executed) cause a computer to perform functions described herein. In some cases, the memory 1430 may include a BIOS that may control basic hardware or software operations, such as interaction with peripheral components or devices, among other things.

[0260]

[0269] The processor 1440 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor 1440 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated with the processor 1440. The processor 1440 may be configured to execute computer-readable instructions stored in a memory (e.g., memory 1430) to cause the device 1405 to perform various functions (e.g., functions or tasks supporting compression and reconstruction of interference distributions). For example, the device 1405 or a component of the device 1405 may include the processor 1440 and the memory 1430 coupled or associated with the processor 1440, where the processor 1440 and the memory 1430 are configured to perform various functions described herein.

[0261]

[0270] The inter-station communications manager 1445 may manage communications with other base stations 105 and may include a controller or scheduler for controlling communications with the UE 115 in cooperation with the other base stations 105. For example, the inter-station communications manager 1445 may coordinate scheduling for transmissions to the UE 115 for various interference mitigation techniques, such as beamforming or joint transmission. In some examples, the inter-station communications manager 1445 may provide an X2 interface in LTE / LTE-A wireless communications network technology to communicate between the base stations 105.

[0262]

[0271] The communications manager 1420 may support wireless communications at the base station in accordance with examples disclosed herein. For example, the communications manager 1420 may be configured as or otherwise support a means for receiving, from a UE, encoded interference information representative of a distribution of measured interference at the UE across a set of interference measurement resources. The communications manager 1420 may be configured as or otherwise support a means for decoding the encoded interference information according to a compression scheme to output the decoded interference information.

[0263]

[0272] By including or configuring a communications manager 1420 in accordance with examples described herein, the device 1405 may support techniques for improved communications reliability, more efficient utilization of communications resources, and improved inter-device coordination through providing dynamic measurement and reporting of interference information at the UE and scheduling of communications that takes into account the interference information at the UE.

[0264]

[0273] In some examples, the communications manager 1420 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the transceiver 1415, one or more antennas 1425, or any combination thereof. Although the communications manager 1420 is shown as a separate component, in some examples, one or more functions described with reference to the communications manager 1420 may be supported or performed by the processor 1440, the memory 1430, the code 1435, or any combination thereof. For example, the code 1435 may include instructions executable by the processor 1440 to cause the device 1405 to perform various aspects of the compression and reconstruction of interference distributions described herein, or the processor 1440 and the memory 1430 may be otherwise configured to perform or support such operations.

[0265]

[0274] FIG. 15 shows a flowchart illustrating a method 1500 for supporting compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. The operations of method 1500 may be implemented by a UE or components thereof as described herein. For example, the operations of method 1500 may be performed by a UE 115 as described with reference to FIGS. 1-10. In some examples, the UE may execute a set of instructions to control functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may perform aspects of the described functions using dedicated hardware.

[0266]

[0275] At 1505, the method may include measuring interference at the UE across the set of interference measurement resources. The operations of 1505 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1505 may be performed by an interference measurement manager 925, as described with reference to FIG.

[0267]

[0276] At 1510, the method may include encoding interference information representing a distribution of interference at the UE based at least in part on the measured interference across the set of interference measurement resources according to a compression scheme. The operations of 1510 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1510 may be performed by a compression manager 930 as described with reference to FIG.

[0268]

[0277] At 1515, the method may include transmitting, to the first network entity, the interference information encoded according to the compression scheme. The operations of 1515 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1515 may be performed by an interference reporting manager 935, as described with reference to FIG.

[0269]

[0278] FIG. 16 shows a flowchart illustrating a method 1600 for supporting compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. The operations of the method 1600 may be implemented by a UE or components thereof as described herein. For example, the operations of the method 1600 may be performed by the UE 115 as described with reference to FIGS. 1-10. In some examples, the UE may execute a set of instructions to control functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may perform aspects of the described functions using dedicated hardware.

[0270]

[0279] At 1605, the method may include receiving, from the first network entity, an indication of a coding configuration configured to encode the interference information. The operations of 1605 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1605 may be performed by a coding configuration manager 940, as described with reference to FIG.

[0271]

[0280] At 1610, the method may include measuring interference at the UE across the set of interference measurement resources. The operations of 1610 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1610 may be performed by an interference measurement manager 925, as described with reference to FIG.

[0272]

[0281] At 1615, the method may include encoding interference information representing a distribution of interference at the UE based at least in part on the measured interference across the set of interference measurement resources according to a compression scheme. The operations of 1615 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1615 may be performed by a compression manager 930 as described with reference to FIG.

[0273]

[0282] At 1620, the method may include transmitting the interference information encoded according to the compression scheme to the first network entity or one or more second network entities associated with the first network entity. The operations of 1620 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1620 may be performed by an interference reporting manager 935, as described with reference to FIG.

[0274]

[0283] FIG. 17 shows a flowchart illustrating a method 1700 for supporting compression and reconstruction of interference distributions in accordance with one or more aspects of the present disclosure. The operations of method 1700 may be implemented by a UE or components thereof as described herein. For example, the operations of method 1700 may be performed by a UE 115 as described with reference to FIGS. 1-10. In some examples, the UE may execute a set of instructions to control functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may perform aspects of the described functions using dedicated hardware.

[0275]

[0284] At 1705, the method may include measuring interference at the UE across the set of interference measurement resources. The operations of 1705 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1705 may be performed by an interference measurement manager 925, as described with reference to FIG.

[0276]

[0285] At 1710, the method may include encoding interference information representing a distribution of interference at the UE based at least in part on the measured interference across the set of interference measurement resources according to a compression scheme. The operations of 1710 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1710 may be performed by a compression manager 930 as described with reference to FIG.

[0277]

[0286] At 1715, the method may include transmitting, to the first network entity, the interference information encoded according to the compression scheme. The operations of 1715 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1715 may be performed by an interference reporting manager 935, as described with reference to FIG.

[0278]

[0287] At 1720, the method may include determining one or more model parameters associated with the compression scheme using an artificial neural network associated with the compression scheme. The operations of 1720 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1720 may be performed by a neural network manager 950, as described with reference to FIG.

[0279]

[0288] At 1725, the method may include transmitting the one or more model parameters to the first network entity or one or more second network entities associated with the first network entity. The operations of 1725 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1725 may be performed by a learned parameter manager 955 as described with reference to FIG.

[0280]

[0289] FIG. 18 shows a flowchart illustrating a method 1800 for supporting compression and reconstruction of interference distributions according to one or more aspects of the present disclosure. The operations of the method 1800 may be implemented by a base station or a component thereof (e.g., a network entity) as described herein. For example, the operations of the method 1800 may be performed by the base station 105 or a network entity as described with reference to FIGS. 1-6 and 11-14. In some examples, the base station or network entity may execute a set of instructions to control functional elements of the base station or network entity to perform the described functions. Additionally or alternatively, the base station or network entity may perform aspects of the described functions using dedicated hardware.

[0281]

[0290] At 1805, the method may include obtaining encoded interference information representing a distribution of interference. The operations of 1805 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1805 may be performed by an interference reporting manager 1325, as described with reference to FIG.

[0282]

[0291] At 1810, the method may include decoding the interference information encoded according to a compression scheme to output decoded interference information. The operations of 1810 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1810 may be performed by an interference report decoding manager 1330, as described with reference to FIG.

[0283]

[0292] FIG. 19 shows a flowchart illustrating a method 1900 for supporting compression and reconstruction of interference distributions according to one or more aspects of the present disclosure. The operations of the method 1900 may be implemented by a base station or a component thereof (e.g., a network entity) as described herein. For example, the operations of the method 1900 may be performed by the base station 105 or a network entity as described with reference to FIGS. 1-6 and 11-14. In some examples, the base station or network entity may execute a set of instructions to control functional elements of the base station or network entity to perform the described functions. Additionally or alternatively, the base station or network entity may perform aspects of the described functions using dedicated hardware.

[0284]

[0293] At 1905, the method may include obtaining encoded interference information representing a distribution of interference. The operations of 1905 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1905 may be performed by an interference reporting manager 1325, as described with reference to FIG.

[0285]

[0294] At 1910, the method may include decoding the interference information encoded according to a compression scheme to output decoded interference information. The operations of 1910 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1910 may be performed by an interference report decoding manager 1330, as described with reference to FIG.

[0286]

[0295] At 1915, the method may include outputting scheduling information for communication at the UE based on the decoded interference information. The operations of 1915 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1915 may be performed by a scheduling manager 1335, as described with reference to FIG.

[0287]

[0296] FIG. 20 shows a flowchart illustrating a method 2000 for supporting compression and reconstruction of interference distributions according to one or more aspects of the present disclosure. The operations of the method 2000 may be implemented by a base station or a component thereof (e.g., a network entity) as described herein. For example, the operations of the method 2000 may be performed by the base station 105 or a network entity as described with reference to FIGS. 1-6 and 11-14. In some examples, the base station or network entity may execute a set of instructions to control functional elements of the base station or network entity to perform the described functions. Additionally or alternatively, the base station or network entity may perform aspects of the described functions using dedicated hardware.

[0288]

[0297] At 2005, the method may include outputting an indication of a coding configuration for encoding the interference information at the UE. The operations of 2005 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 2005 may be performed by a coding configuration manager 1340, as described with reference to FIG.

[0289]

[0298] At 2010, the method may include obtaining coded interference information coded according to a compression scheme, the interference information representing a distribution of interference across a set of resources. The operations of 2010 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 2010 may be performed by an interference reporting manager 1325 as described with reference to FIG.

[0290]

[0299] At 2015, the method may include decoding the interference information encoded according to a compression scheme to output decoded interference information. The operations of 2015 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 2015 may be performed by an interference report decoding manager 1330, as described with reference to FIG.

[0291]

[0300] FIG. 21 illustrates an example of a network architecture 2100 (e.g., a disaggregated base station architecture, a disaggregated RAN architecture) supporting compression and reconstruction of interference distribution according to one or more aspects of the present disclosure. The network architecture 2100 may illustrate an example for implementing one or more aspects of the wireless communication system 100. The network architecture 2100 may include one or more CUs 160-a that may directly communicate with the core network 130-a via a backhaul communication link 120-a or indirectly communicate with the core network 130-a via one or more disaggregated network entities (e.g., a quasi-RT RIC 175-b via an E2 link, or a non-RT RIC 175-a associated with an SMO 180-a (SMO framework), or both). The CUs 160-a may communicate with one or more DUs 165-a via respective midhaul communication links 162-a (e.g., an F1 interface). The DU 165-a may communicate with one or more RUs 170-a via respective fronthaul communication links 168-a. The RUs 170-a may be associated with respective coverage areas 110-c and may communicate with the UE 115-h via one or more communication links 125-a. In some implementations, the UE 115-h may be served by multiple RUs 170-a simultaneously.

[0292]

[0301] Each of the network entities (e.g., CU 160-a, DU 165-a, RU 170-a, non-RT RIC 175-a, quasi-RT RIC 175-b, SMO 180-a, Open Cloud (O-Cloud) 2105, Open eNBs (O-eNBs) 2110) of the network architecture 2100 may include one or more interfaces or may be coupled with one or more interfaces configured to receive or transmit signals (e.g., data, information) over a wired or wireless transmission medium. Each network, or an associated processor that provides instructions to the network entity's interfaces, may be configured to communicate with one or more of the other network entities over the transmission medium. For example, a network entity may include a wired interface configured to receive or transmit signals to one or more of the other network entities over a wired transmission medium. Additionally or alternatively, a network entity may include a wireless interface, which may include a receiver, transmitter, or transceiver (such as an RF transceiver), configured to receive and / or transmit signals to one or more of the other network entities over a wireless transmission medium.

[0293]

[0302] In some examples, the CU 160-a may host one or more higher layer control functions. Such control functions may include RRC, PDCP, SDAP, etc. Each control function may be implemented with an interface configured to communicate signals with other control functions hosted by the CU 160-a. The CU 160-a may be configured to handle user plane functions (e.g., CU-UP), control plane functions (e.g., CU-CP), or a combination thereof. In some examples, the CU 160-a may be logically divided into one or more CU-UP units and one or more CU-CP units. The CU-UP units, when implemented in an O-RAN configuration, may communicate bidirectionally with the CU-CP units via an interface, such as an E1 interface. The CU 160-a may be implemented to communicate with the DU 165-a, as necessary, for network control and signaling.

[0294]

[0303] The DU 165-a may correspond to a logical unit including one or more functions (e.g., base station function, RAN function) for controlling the operation of one or more RUs 170-a. In some examples, the DU 165-a may at least partially host one or more of the RLC layer, the MAC layer, and one or more aspects of the PHY layer (e.g., higher PHY layers, such as modules for FEC encoding and decoding, scrambling, modulation and demodulation, etc.), depending at least in part on a functional division, such as that defined by the 3rd Generation Partnership Project (3GPP). In some examples, the DU 165-a may further host one or more lower PHY layers. Each layer (or module) may be implemented with an interface configured to communicate signals with other layers hosted by the DU 165-a or with control functions hosted by the CU 160-a.

[0295]

[0304] In some examples, the low layer functions may be hosted by one or more RUs 170-a. For example, the RUs 170-a controlled by the DUs 165-a may correspond to logical nodes hosting RF processing functions, or low PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.), or both, based at least in part on a functional division such as a lower layer functional division. In such an architecture, the RUs 170-a may be implemented to handle over-the-air (OTA) communications with one or more UEs 115-h. In some implementations, real-time and non-real-time aspects of control and user plane communications with the RUs 170-a may be controlled by the corresponding DUs 165-a. In some examples, such a configuration may enable the DUs 165-a and CUs 160-a to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

[0296]

[0305] The SMO 180-a may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network entities. For non-virtualized network entities, the SMO 180-a may be configured to support deployment of dedicated physical resources for RAN coverage requirements that may be managed via an operation and maintenance interface (such as an O1 interface). For virtualized network entities, the SMO 180-a may be configured to interact with a cloud computing platform (e.g., O-Cloud 2105) to perform network entity lifecycle management (e.g., instantiate virtualized network entities) via a cloud computing platform interface (e.g., an O2 interface). Such virtualized network entities may include, but are not limited to, the CU 160-a, the DU 165-a, the RU 170-a, and the quasi-RT RIC 175-b. In some implementations, the SMO 180-a may communicate with components configured according to a 4G RAN (e.g., via an O1 interface). Additionally or alternatively, in some implementations, the SMO 180-a may communicate directly with one or more RUs 170-a via an O1 interface. The SMO 180-a may also include a non-RT RIC 175-a configured to support the functionality of the SMO 180-a.

[0297]

[0306] The non-RT RIC 175-a may be configured to include logic functions that enable non-real-time control and optimization of RAN elements and RAN resources, artificial intelligence (AI) or machine learning (ML) workflows including model training and updates, or policy-based guidance of applications / features in the quasi-RT RIC 175-b. The non-RT RIC 175-a may be coupled to or in communication with the quasi-RT RIC 175-b (e.g., via an A1 interface). The quasi-RT RIC 175-b may be configured to include logic functions that enable near real-time control and optimization of RAN elements and RAN resources via data collection and action via one or more CUs 160-a, one or more DUs 165-a, or both, and an interface connecting the O-eNB 2110 to the quasi-RT RIC 175-b (e.g., via an E2 interface).

[0298]

[0307] In some examples, the non-RT RIC 175-a may receive parameters or external enrichment information from an external server to generate the AI / ML models deployed to the quasi-RT RIC 175-b. Such information may be utilized by the quasi-RT RIC 175-b and may be received at the SMO 180-a or the non-RT RIC 175-a from non-network data sources or from network functions. In some examples, the non-RT RIC 175-a or the quasi-RT RIC 175-b may be configured to adjust RAN behavior or performance. For example, the non-RT RIC 175-a may employ the AI / ML models to monitor long-term trends and patterns regarding performance and implement corrective actions through the SMO 180-a (e.g., reconfiguration via O1) or by creating RAN management policies (e.g., A1 policies).

[0299]

[0308] The following provides a summary of embodiments of the present disclosure.

[0300]

[0309] Aspect 1: A method for wireless communication in a UE, the method including: measuring interference at the UE across a set of interference measurement resources; encoding interference information representing a distribution of interference at the UE based on the measured interference across the set of interference measurement resources according to a compression scheme; and transmitting the interference information encoded according to the compression scheme to a first network entity.

[0301]

[0310] Aspect 2: The method of aspect 1, wherein the distribution of interference at the UE comprises a probability mass function for a set of resources in time, frequency, and / or space.

[0302]

[0311] Aspect 3: The method of aspect 2, wherein the set of resources in time, frequency, and / or space includes a set of interference measurement resources.

[0303]

[0312] Aspect 4: The method of aspect 2, wherein the set of resources in time, frequency, and / or space includes resources prior to the set of interference measurement resources, and the distribution of interference at the UE is based at least in part on measured interference across the set of interference measurement resources.

[0304]

[0313] Aspect 5: The method of aspect 2, wherein the set of resources in time, frequency, and / or space includes resources subsequent to the set of interference measurement resources, and the distribution of interference at the UE is predicted based at least in part on measured interference across the set of interference measurement resources.

[0305]

[0314] Aspect 6: The method of any of aspects 1 to 5, wherein encoding according to a compression scheme includes generating a condensed estimated or predicted interference distribution across the set of interference measurement resources.

[0306]

[0315] Aspect 7: The method of any of aspects 1 to 6, wherein the compression scheme comprises a codeword-based compression scheme or an artificial neural network-based compression scheme.

[0307]

[0316] Aspect 8: A method as described in any of aspects 1 to 7, wherein encoding according to a compression scheme includes generating a mean vector and a covariance matrix of latent random variables representing a distribution of interference across a set of interference measurement resources, the set of interference measurement resources including two or more sets of time resources, frequency resources, or spatial resources.

[0308]

[0317] Aspect 9: The method of any of aspects 1 to 8, further comprising receiving an indication of a coding configuration for coding the interference information from the first network entity or one or more second network entities associated with the first network entity.

[0309]

[0318] Aspect 10: The method of aspect 9, further comprising: transmitting an indication of the UE's capability for encoding interference information to the first network entity or one or more second network entities associated with the first network entity, wherein receiving the indication of the coding configuration for encoding the interference information comprises receiving an indication of an index associated with the coding configuration from the first network entity or one or more second network entities associated with the first network entity in response to transmitting the indication of the UE's capability for encoding interference information, wherein a set of coding configurations including the coding configuration is associated with a set of indexes including the index.

[0310]

[0319] Example 11: The method of example 9 or 10, wherein the encoding configuration includes configuration for one of an autoencoder or an artificial neural network.

[0311]

[0320] Aspect 12: A method according to any of aspects 1 to 11, further comprising: selecting one coding configuration from a set of coding configurations for coding interference information, each coding configuration in the set of coding configurations being associated with a respective index in a set of indexes; and transmitting an indication of one index in the set of indexes associated with the selected coding configuration to the first network entity or one or more second network entities associated with the first network entity.

[0312]

[0321] Aspect 13: The method of any of aspects 1 to 12, further comprising receiving an indication of one or more parameters associated with the compression scheme from the first network entity or one or more second network entities associated with the first network entity.

[0313]

[0322] Aspect 14: The method of aspect 13, wherein the one or more parameters associated with the compression scheme include a code size, a number of layers, a number of nodes per layer, a loss function, or a combination thereof.

[0314]

[0323] Aspect 15: The method of any of aspects 1 to 14, further comprising receiving an indication of the set of interference measurement resources from the first network entity or one or more second network entities associated with the first network entity.

[0315]

[0324] Aspect 16: The method of any of aspects 1 to 15, further comprising receiving, from the first network entity or one or more second network entities associated with the first network entity, an indication of one or more parameters associated with measuring interference at the UE across the set of interference measurement resources.

[0316]

[0325] Aspect 17: The method of aspect 16, wherein the one or more parameters associated with measuring interference at a UE across a set of interference measurement resources include frequency granularity, time granularity, spatial granularity, or a combination thereof.

[0317]

[0326] Aspect 18: The method of any of aspects 1 to 17, further comprising receiving, from the first network entity or one or more second network entities associated with the first network entity, an indication of an input format for encoding interference information representing a distribution of interference at the UE.

[0318]

[0327] Aspect 19: The method of any one of aspects 1 to 18, wherein transmitting the interference information coded according to the compression scheme includes transmitting a channel state feedback report including the coded interference information coded according to the compression scheme.

[0319]

[0328] Aspect 20: A method as described in any of aspects 1 to 19, further comprising: determining one or more model parameters associated with the compression scheme using an artificial neural network associated with the compression scheme; and transmitting the one or more model parameters to the first network entity or one or more second network entities associated with the first network entity.

[0320]

[0329] Aspect 21: The method of any of aspects 1 to 20, further comprising receiving one or more parameters associated with the compression scheme based at least in part on transmitting the one or more model parameters from the first network entity or one or more second network entities associated with the first network entity.

[0321]

[0330] Example 22: The method of any of examples 1 to 120, wherein the interference at the UE includes interference plus noise, and the distribution of the interference includes a distribution of the interference plus noise across the set of interference measurement resources.

[0322]

[0331] Aspect 23: A method for wireless communication in a network entity, the method including: obtaining encoded interference information representing a distribution of interference; and decoding the encoded interference information according to a compression scheme to output decoded interference information.

[0323]

[0332] Example 24: The method of example 23, further comprising: outputting scheduling information for communication at the UE based at least in part on the decoded interference information.

[0324]

[0333] Aspect 25: The method of aspect 23, wherein the coded interference information includes a mean vector and a covariance matrix of latent random variables representing a distribution of interference at the UE across the set of interference measurement resources, the method further including: generating samples based on the mean vector and the covariance matrix; and decoding the coded interference information based at least in part on the samples.

[0325]

[0334] Example 26: The method of example 23 or 24, further comprising: outputting an indication of a coding configuration for coding the interference information at the UE.

[0326]

[0335] Aspect 27: The method of aspect 26, further comprising obtaining an indication of a capability of the UE for encoding interference information, wherein outputting the indication of the encoding configuration comprises outputting an indication of an index associated with the encoding configuration based at least in part on receiving the indication of the capability of the UE for encoding interference information, wherein a set of encoding configurations including the encoding configuration is associated with a set of indexes including the index.

[0327]

[0336] Example 28: The method of example 26 or 27, wherein the encoding configuration includes configuration for one of an autoencoder or an artificial neural network.

[0328]

[0337] Aspect 29: A method described in any of aspects 23 to 28, further comprising obtaining an indication of one index of a set of indexes associated with a selected encoding configuration, each encoding configuration of the set of encoding configurations being associated with a respective index of the set of indexes.

[0329]

[0338] Example 30: The method of any of examples 23 to 29, further comprising outputting an indication of one or more parameters associated with the compression scheme.

[0330]

[0339] Aspect 31: The method of aspect 30, wherein the one or more parameters associated with the compression scheme include a code size, a number of layers, a number of nodes per layer, a loss function, or a combination thereof.

[0331]

[0340] Example 32: The method of any of Examples 23 to 31, further comprising: outputting an indication of one or more parameters associated with measuring interference across the set of interference measurement resources.

[0332]

[0341] Aspect 33: The method of aspect 32, wherein the one or more parameters associated with measuring interference across a set of interference measurement resources include frequency granularity, time granularity, spatial granularity, or a combination thereof.

[0333]

[0342] Example 34: The method according to any one of Examples 23 to 33, further comprising outputting an instruction for an input format of the interference information representing a distribution of interference.

[0334]

[0343] Example 35: The method of any of Examples 23 to 34, wherein obtaining the coded interference information includes obtaining a channel state feedback report including the coded interference information, the channel state feedback report being coded according to a compression scheme.

[0335]

[0344] Aspect 36: A method described in any of aspects 23 to 35, further comprising: determining one or more model parameters associated with the compression scheme using an artificial neural network associated with the compression scheme; and outputting one or more parameters associated with the compression scheme based at least in part on the one or more model parameters.

[0336]

[0345] Aspect 37: A method described in any of aspects 23 to 36, further comprising obtaining one or more model parameters associated with the compression scheme, and outputting one or more parameters associated with the compression scheme based at least in part on the one or more model parameters.

[0337]

[0346] Example 38: The method of any of examples 23 to 37, wherein the distribution of interference includes a distribution of interference plus noise across the set of interference measurement resources.

[0338]

[0347] Aspect 39: An apparatus for wireless communication in a UE, comprising: a processor; and a memory coupled to the processor, wherein the processor is configured to perform a method as recited in any of aspects 1-22.

[0339]

[0348] Aspect 40: An apparatus for wireless communication in a UE, comprising at least one means for performing the method according to any one of aspects 1-22.

[0340]

[0349] Aspect 41: A non-transitory computer-readable medium storing code for wireless communication in a UE, the code including instructions executable by a processor to perform a method as described in any of aspects 1-22.

[0341]

[0350] Aspect 42: An apparatus for wireless communication in a network entity, comprising: a processor configured to cause the apparatus to perform the method according to any of aspects 23-38.

[0342]

[0351] Example 43: An apparatus for wireless communication in a network entity, comprising at least one means for performing the method according to any of examples 23-38.

[0343]

[0352] Aspect 44: A non-transitory computer-readable medium storing code for wireless communication in a network entity, the code including instructions executable by a processor to perform a method as described in any of aspects 23-38.

[0344]

[0353] It should be noted that the methods described herein are described in terms of possible implementations, that the acts and steps may be rearranged or otherwise modified, and that other implementations are possible. Furthermore, aspects from two or more of the methods may be combined.

[0345]

[0354] Although aspects of LTE, LTE-A, LTE-A Pro, or NR systems may be described as examples, and LTE, LTE-A, LTE-A Pro, or NR terminology may be used in much of the description, the techniques described herein may be applicable to other than LTE, LTE-A, LTE-A Pro, or NR networks. For example, the techniques described may be applicable to various other wireless communication systems, such as Ultra Mobile Broadband (UMB), Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM, and other systems and radio technologies not explicitly mentioned herein.

[0346]

[0355] The information and signals described herein may be represented using any of a wide variety of technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips that may be referred to throughout the description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0347]

[0356] The various example blocks and components described with respect to the disclosure herein may be implemented or performed using a general purpose processor, a DSP, an ASIC, a CPU, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any processor, controller, microcontroller, or state machine. A processor may be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0348]

[0357] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over a computer-readable medium as one or more instructions or code. Other examples and implementations are within the scope of this disclosure and the appended claims. For example, due to the nature of software, the functions described herein may be implemented using software executed by a processor, hardware, firmware, hardwiring, or any combination thereof. Features implementing the functions may also be physically located in various locations, including being distributed such that parts of the functions are implemented in different physical locations.

[0349]

[0358] Computer-readable media includes both non-transitory computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another. Non-transitory storage media may be any available medium that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, non-transitory computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM), flash memory, compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general purpose or special purpose computer or a general purpose or special purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included within the definition of computer readable media. As used herein, disk and disc include CDs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically and discs reproduce data optically using lasers. Combinations of the above are also included within the scope of computer readable media.

[0350]

[0359] As used herein, including in the claims, "or" as used in a list of items (e.g., a list of items followed by a phrase such as "at least one of" or "one or more of") indicates an inclusive list, such as, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, the phrase "based on" as used herein should not be construed as a reference to a closed set of conditions. For example, an exemplary step described as "based on condition A" may be based on both condition A and condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase "based on" is to be interpreted the same as the phrase "based at least in part on."

[0351]

[0360] The terms "determine" or "determining" encompass a wide variety of actions, and thus "determining" can include calculating, computing, processing, deriving, investigating, looking up (such as via a lookup in a table, database, or another data structure), ascertaining, etc. "Determining" can also include receiving (such as receiving information), accessing (such as accessing data in a memory), etc. "Determining" can also include resolving, selecting, choosing, establishing, and other similar acts.

[0352]

[0361] As used herein, including the claims, the term "set" refers to a grouping of one or more.

[0353]

[0362] In the accompanying figures, similar components or features may have the same reference label. Furthermore, various components of the same type may be distinguished by following the reference label with a dash and a second label that distinguishes between the similar components. If only a first reference label is used herein, the description is applicable to any of the similar components having the same first reference label, regardless of a second reference label, or other subsequent reference label.

[0354]

[0363] The description set forth herein with respect to the accompanying drawings describes exemplary configurations and does not necessarily represent all examples that may be implemented or fall within the scope of the claims. The term "example" as used herein means "serving as an example, instance, or illustration" and does not mean "preferred" or "advantageous over other examples." The detailed description includes specific details for the purposes of providing an understanding of the described techniques. However, these techniques may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described examples.

[0355]

[0364] The description herein is provided to enable any person skilled in the art to make or use the disclosure. Various modifications of the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. 1. A method for wireless communication in a user equipment (UE), comprising: receiving, from a first network entity, an indication of one or more parameters associated with a compression scheme; measuring interference at the UE across a set of interference measurement resources; encoding interference information representing a distribution of interference at the UE based at least in part on the measured interference across the set of interference measurement resources according to the compression scheme, wherein the set of interference measurement resources includes a channel state information (CSI) reference signal (CSI-RS), a CSI interference measurement (CSI-IM), or an interference measurement resource (IMR), and the IMR comprises a time-frequency resource. transmitting, to the first network entity, the interference information encoded according to the compression scheme.

2. The method of claim 1 , wherein the interference at the UE comprises interference plus noise, and the distribution of the interference comprises a distribution of interference plus noise across the set of interference measurement resources.

3. 2. The method of claim 1, wherein the distribution of interference at the UE comprises a probability mass function for a set of resources in time, frequency, and / or space, the set of resources in time, frequency, and / or space including the set of interference measurement resources, or the set of resources in time, frequency, and / or space including resources before the set of interference measurement resources, and the distribution of interference at the UE is based at least in part on the measured interference across the set of interference measurement resources, or the set of resources in time, frequency, and / or space including resources after the set of interference measurement resources, and the distribution of interference at the UE is predicted based at least in part on the measured interference across the set of interference measurement resources.

4. The encoding in accordance with the compression method includes: generating a compressed estimated or predicted interference distribution over the set of interference measurement resources, or the encoding in accordance with the compression scheme, 2. The method of claim 1, comprising generating a mean vector and a covariance matrix representing a distribution of the interference at the UE across the set of interference measurement resources, the set of interference measurement resources comprising two or more sets of time resources, frequency resources, or spatial resources.

5. receiving, from the first network entity or one or more second network entities associated with the first network entity, an indication of a coding configuration for encoding the interference information; and transmitting an indication of a capability of the UE to encode interference information to the first network entity or one or more second network entities associated with the first network entity, wherein the receiving the indication of the encoding configuration for encoding the interference information comprises:

2. The method of claim 1, comprising receiving, from the first network entity or one or more second network entities associated with the first network entity, an indication of an index associated with the coding configuration in response to the transmitting of the indication of the capability of the UE to encode interference information, wherein a set of coding configurations including the coding configuration is associated with a set of indexes including the index, or the coding configuration comprises a configuration for one of an autoencoder or an artificial neural network.

6. The method described in claim 1, wherein the one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof.

7. 2. The method of claim 1, further comprising receiving an indication of the set of interference measurement resources from the first network entity or one or more second network entities associated with the first network entity.

8. receiving, from the first network entity or one or more second network entities associated with the first network entity, an indication of one or more parameters associated with measuring the interference at the UE across the set of interference measurement resources; 2. The method of claim 1, wherein the one or more parameters associated with measuring the interference at the UE across the set of interference measurement resources include frequency granularity, time granularity, spatial granularity, or a combination thereof.

9. 1. A method for wireless communication in a network entity, comprising: transmitting to a user equipment (UE) an indication of one or more parameters associated with the compression scheme; obtaining coded interference information representing a distribution of interference, the distribution of interference comprising a distribution over a set of interference measurement resources, the set of interference measurement resources including a channel state information (CSI) reference signal (CSI-RS), a CSI interference measurement (CSI-IM), or an interference measurement resource (IMR), the IMR comprising a time-frequency resource; and decoding the encoded interference information according to the compression scheme to output decoded interference information.

10. The coded interference information comprises a mean vector and a covariance matrix of latent random variables representing a distribution of the interference at a user equipment (UE) over a set of interference measurement resources, and the method comprises: generating samples based on the mean vector and the covariance matrix; The method of claim 9 , further comprising: decoding the encoded interference information based at least in part on the samples.

11. outputting an indication of a coding configuration for coding the interference information at a user equipment (UE); and obtaining an indication of a capability of the UE to encode interference information, wherein the outputting the indication of the encoding configuration further comprises:

10. The method of claim 9, comprising outputting an indication of an index associated with the coding configuration based at least in part on the indication of the capability of the UE to encode interference information, wherein a set of coding configurations including the coding configuration is associated with a set of indexes including the index, or the coding configuration comprises a configuration for one of an autoencoder or an artificial neural network.

12. The method described in claim 9, wherein the one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof.

13. outputting an indication of one or more parameters associated with measuring interference across the set of interference measurement resources; the one or more parameters associated with measuring interference across the set of interference measurement resources include frequency granularity, time granularity, spatial granularity, or a combination thereof; or The method of claim 9 , further comprising outputting an input format indication for encoding the interference information representing the distribution of interference.

14. 1. An apparatus for wireless communication in a user equipment (UE), comprising: means for receiving, from a first network entity, an indication of one or more parameters associated with a compression scheme; means for measuring interference at the UE across a set of interference measurement resources; means for encoding interference information representing a distribution of interference at the UE based at least in part on the measured interference across the set of interference measurement resources according to a compression scheme, wherein the set of interference measurement resources includes a Channel State Information (CSI) Reference Signal (CSI-RS), a CSI Interference Measurement (CSI-IM), or an Interference Measurement Resource (IMR), the IMR comprising a time-frequency resource; means for transmitting, to a first network entity, the interference information encoded according to the compression scheme.

15. 1. An apparatus for wireless communication in a network entity, comprising: means for transmitting, to a user equipment (UE), an indication of one or more parameters associated with the compression scheme; means for obtaining coded interference information representing a distribution of interference, the distribution of interference comprising a distribution over a set of interference measurement resources, the set of interference measurement resources including a channel state information (CSI) reference signal (CSI-RS), a CSI interference measurement (CSI-IM), or an interference measurement resource (IMR), the IMR comprising a time-frequency resource; means for decoding the encoded interference information according to a compression scheme to output decoded interference information.

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